The Monte Carlo method is a powerful and simple tool for generating statistical information. Typical examples of its gas turbine applications are in the uncertainty analysis of measurements, the probability of achieving an engine design target and production tolerance estimates. Engine tests evaluate much more than overall characteristics in terms of thrust and specific fuel consumption; the main objective of performance testing is the efficiency of the engine components. Both random and systematic measurement errors affect the accuracy of the analysis result. When a new engine is designed, there is always uncertainty about the component performances achievable. This transfers to an uncertainty in the overall engine performance. For example, even though the design target in specific fuel consumption is met ostensibly, the cycle or guaranteed value may need to be modified to improve the level of confidence. If we simulate a batch of engines with randomly distributed properties, as the result of component manufacturing tolerances we can predict the effects of various combinations of these on thrust or shaft power, efficiency and specific fuel consumption. The influence of variations in internal air systems can also be accounted for. GasTurb makes Monte Carlo simulations easy, both for cycle design and off-design applications.