Monte Carlo Simulation in Capital Budgeting Process provides better understanding of risk and uncertainty in discounted cash flow analysis (DCF). The traditional methods of the risk analysis in Capital Budgeting Process such as sensitivity analysis and scenario analysis do not provide information of probability of the failure or success of a capital investment projects. The use of the Monte Carlo Simulation in decision making process provides probability distributions of all predetermined variables (NPV, IRR), respectively estimation of probability of success of a project. Among other results Monte Carlo Simulation quantifies how each assumption affects the NPV or IRR of the project. Although Monte Carlo Simulation can help managing risk and uncertainty in the decision making process, this method is not without limitations. Simulations can lead to misleading results if inappropriate inputs are entered into the model. The conclusion is that only the combination of the traditional methods of risk analysis and the Monte Carlo Simulation in Capital Budgeting Process ensure the optimal selection of capital projects. However, it is important to emphasize that the quality of the results is directly related and proportional to the quality of inputs used, in all the methods.