This study proposes an intelligent system to perform fault diagnosis actions in ship machinery systems. Considering the cost limitations, the main goal is to optimize the machinery system availability. The model takes the advantage of a classification tool based on support vector machines principles. Statistical assumptions are also considered for validity of the analysis. The test and statistical demonstration phases are also supported with the data, gathered from the specifically created operational scenarios in ship engine room simulator. The results of the study can be adopted to conceptualize a continuous monitoring tool on ship system reliability. Furthermore, real case application potential of the proposed intelligent fault diagnosis system is comprehensively discussed.