During engine overhaul process, many different engines are disassembled at the piece part level, resulting in thousands of components that must be cleaned, inspected, repaired and reassembled on the original engine. Considering that many engines from different customers are processed simultaneously and the parts go through several specific and highly complex processes, the consequences of a possible failure exists. This paper discusses the application of Bayesian Networks in the analysis of the risks present in all steps of the engine overhaul process. As a methodological approach, a repair station process was entirely mapped out, the risk database was reviewed, and the risks analysed, considering their impact on the different processes steps. A software was used for probabilistic risk analysis using Bayesian Networks, it allowed the combination of risks raised by different tools (PFMEA, Predictive Systematic Analysis and Voluntary Reports). A sensitivity analyses was performed to identify which activities within the various processes would have a significant impact during engine overhaul process leading to overall risk of failure. The sensitivity analysis allowed the identification of risks that could generate the highest costs, making the decision-making process easier by helping in the prioritization and allocation of resources to the solution of the most significant risks. The benefits of the technique are evident and have practical implications for specialists dealing with risk factor identification and quantitative analysis during jet engines overhaul process.