Abstarct: Risk assessment is able to provide valuable information for decision making in risk reduction, optimization of control systems and planning to provide a favorable response. In this study, a hybrid risk assessment method with the capability of analyzing quantitative and qualitative data related to processes baxsed on multi-criteria decision making technique and Bayesian statistics is presented. First, the risks involved in the process and its associated factors are identified through a targeted risk assessment approach. The identified risks are first quantitatively and finally quantified using the Bayesian network of relationships between them to assess the risks involved in the process. Bayesian networks provide a decision support system for many of the problems associated with uncertainty and possible reasoning, and are used as an efficient way of modeling decision problems in uncertainty situations. Next, the production process scenarios (schemes) are analyzed using the Bayesian analysis network. Finally, each of the production process scenarios is evaluated and ranked by the Macbeth Multi Criteria Decision Making Method. The proposed model is implemented as a case study for process risk analysis in a power plant. The results of the research indicate the effectiveness of the proposed method for risk analysis with respect to its achievements.