Modern measuring instruments as highly technological, precise, multi-functional tools today are complex systems, and estimation of their uncertainty turns into a non-trivial task of science. To provide information about the probability of results, their convergence, and reproducibility, it is necessary to analyze the task-oriented measurement uncertainty. As an approach to determining the uncertainty of complex systems, to avoid the need for professionally experienced personnel and expensive "artifacts" used for evaluation, there is a method of a so-called virtual measuring instrument. In this method, the measurement process is simulated, taking into account the influence of the main disturbance parameters and conducting statistical analysis using the Monte Carlo approach. All characteristics of virtual modules repeat the properties of real devices but allow quick and qualitative evaluation of environmental parameters' effect on the accuracy, as well as the uncertainty of measurement. It allows us to evaluate the correctness of the result under the present conditions. The measurement uncertainty is usually caused by several major sources. Uncertainty depends on the method of measurement, but there are still common factors, i.e. uncertainty caused by measuring instruments, methods, operators, and environment. Among environmental influences, it is important to highlight - the change of light and temperature, which can vary widely variate at the production process, and at the same time have a crucial impact on the uncertainty of measurement. The paper presents a virtual measurement instrument method and its known implementations
 JCGM 100:2008, Evaluation of measurement data – Guide to the expression of uncertainty in measurement JCGM 100:2008 (GUM 1995 with minor corrections). 2008, Paris: BIPM Joint Committee for Guides in Metrology. [Online]. Available: https://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf. Accessed on Aug 7, 2020.
 EN ISO 15530-3:2011 Geometrical product specifications (GPS) - Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement - Part 3: Use of calibrated workpieces or measurement standards.
 ISO/TS 15530-4:2008 Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 4: Evaluating task-specific measurement uncertainty using simulation.
 K. Sommer, B. Siebert, "Praxisgerechtes bestimmen der messunsicherheit nach gum (practical determination of the measurement uncertainty under gum)", tm–Technisches Messen/Sensoren, Geräte, Systeme, no. 2(71), pp.52-66, 2004.
 JCGM 101:2008 Evaluation of measurement data-supplement 1 to the guide to the expression of uncertainty in measurement-propagation of distributions using a Monte Carlo method [Online]. Available: https://www.bipm.org/utils/common/documents/jcgm/JCGM_101_2008_E.pdf. Accessed on Aug 7, 2020.
 R. Hanus, B. Stadnyk, A Kowalczyk, “Virtual instrumentation for delay measuring using the power spectral density method”, Herald of the Lviv Polytechnic National University: Automation, Measurement and Control, no. 475, pp. 3–8, 2003.
 A. Ozghovych, I. Likhnovskyi, O. Tyshchenko, A. Kuzii, “Construction of scales of virtual measuring instruments”, Measuring Equipment and Metrology, vol. 75, pp. 27-3, 2014.
 M. Trenk, M. Franke and H. Schwenke, “The ‘Virtual CMM’a software tool for uncertainty evaluation—practical application in an accredited calibration lab”, in Proc. of ASPE: Uncertainty Analysis in Measurement and Design, 2004.
 Y. Novikov, "Virtual Measuring Instruments", Journal of Surface Investigation. X-ray, Synchrotron and Neutron Techniques, vol.10, p. 68–75, 2016.
 Y. Novikov, "Virtual raster electronic microscope. 2. Principles of device building”, Russian Microelectronics, vol. 42, no. 4, p. 262-270, 2013.
 S. Mordechai, Applications of Monte Carlo Methods in Science and Engineering, London, UK, IntechOpen, 2011.
 S. Thompson, "Random number generators for C++ performance tested". [Online] Available: http://thompsonsed.co.uk/random-number-generators-for-c-performance-tested. Accessed on Aug 7, 2020.
 Y. A. Novikov, "Virtual raster electronic microscope. 4. Realisation using simulator”, Russian Microelectronics, vol. 43, no. 6, pp. 456-467, 2014.
 M. O'Neill, "PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation". [Online]. Available: https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf. 2014. Accessed on Aug 7, 2020.
 D. Flack , "Good Practice Guide No. 130. Coordinate measuring machine task-specific measurement uncertainties", 2013. [Online]. Available: https://www.npl.co.uk/special-pages/guides/gpg130_machine. Accessed on Aug 7, 2020..
 E. Trapet, "Traceability of Coordinate Measurements According to the Method of the Virtual Measuring Machine: Part 2 of the Final Report Project MAT1-CT94-0076", Braunschweig, Physikalisch-Technische Bundesanstalt, vol. 35, p. 147, 1999.
 Y. Novikov, "Virtual raster electronic microscope. 1. Goals and objectives of virtual instruments”, Russian Microelectronics, vol. 42, no. 1, pp. 34-41, 2013.
 Y. Novikov, "Virtual raster electronic microscope. 3. Semi-empirical model of REM signal forming”, Russian Microelectronics, vol. 43, no. 4, pp. 263-274, 2014.
 Y. Novikov, "Virtual raster electronic microscope. 5. Application of nanotechnologies in micro and nanoelectronics”, Russian Microelectronics, vol. 44, no. 4, pp. 306-320, 2015.
 PTB Competence Center "Metrology for Virtual Measuring Instruments"(VirtMet). [Online]. Available: https://www.ptb.de/cms/fileadmin/internet/PSt/pst1/VirtMet_Whitepaper_pu.... Accessed on Aug 7, 2020.