Process simulation is essential to the economic evaluation and the reliability or safety analysis of a chemical process. However, conventional simulators do not provide users with the information on the accuracy of the results or on the effects of the uncertainties in the data used in the simulation. Therefore, a new simulation method is required which deals with uncertainties in the input variables and in the model parameters. A methodology for stochastic simulation is proposed in this paper, which is based on Monte Carlo simulation. The results of sensitivity analysis numerically and graphically show the trend of the change in the uncertainties of the process variables, changes in the importance of the variables, and the relations between the variables. The proposed approach was implemented in a general purpose dynamic process simulator, MOSA, and showed good applicabilities for chemical processes with various uncertainties. One of the most important advantages of the proposed method is that it can use the deterministic models used in the conventional simulators without any modifications.