Fault isolation is the art of localizing faults in a process, given observations from it. To do this, a model describing the relation between faults and observations is needed. In this paper we focus on learning such models both from training data and from prior knowledge. There are several challenges in learning fault isolators. The number of data, as well as the available computing resources, are often limited and there may be previously unobserved fault patterns. To meet these challenges we take on a Bayesian approach. We compare five different methods for learning in fault isolation, and evaluate their performance on a real fault isolation problem; the diagnosis of an automotive engine.