The frequency of occurrence of an accident is a key aspect in the risk assessment field. Vari- ables such as the human factor (HF), which is a major cause of undesired events in process industries, are usually not considered explicitly, mainly due to the uncertainty generated due to the lack of knowledge and the complexity associated to it. In this work, failure frequencies are modified through Monte Carlo (MC) simulation includ- ing the uncertainty generated by HF. MC is one of the most commonly approach used for uncertainty assessment based on probability distribution functions that represent all the variables included in the model. This technique has been also proved to be very useful in the risk assessment field. The model takes into account the uncertainty and variability generated by several HF variables. In order to test the model, it has been applied to two real case studies, obtaining new frequency values for the different scenarios. Together with the consequences assessment, new isorisk curves were plotted. Since the uncertainty generated by the HF has now been taken in to account through MC simulation, these new values are more realistic and accurate. As a result, an improvement of the final risk assessment is achieved.