The development of fault diagnosis systems able to early detect and identify any malfunctioning is of great importance towards the diffusion of energy conversion plants based on solid oxide fuel cells. Because the traditional model-based schemes for the diagnosis demonstrated a poor fault identification capability (especially when many operating conditions and fault sizes are combined), hybrid schemes were proposed that, incorporating pattern recognition classifiers, achieved significant performance improvements. However, like in the model-based case, the hybrid schemes require the running of the plant model in parallel to the real plant functioning. In this study, a data-driven fault diagnosis scheme is presented, in which the plant model is used only off-line, for the training of the classifier. Both support vector machine and random forest classifiers are proposed and tested. It is demonstrated that, when a support vector machine classifier is deployed, the data driven system outperforms the hybrid systems in both fault detection and fault identification. High-level performance is achieved despite the large number of combinations among working conditions and fault sizes used for testing the system.