Due to the increasing demands concerning reliability, safety and economy of technical processes, the on-line monitoring of induction motors is an important topic in the engineering field. In this paper, a model-based approach for fault detection and diagnosis of nonlinear processes is employed. The supervision of nonlinear systems is often very difficult in view of the lack of accurate models. However, neuro-fuzzy models may help to cope with this problem, as they can be trained from measured data. In this contribution the application of a multi-model approach for fault detection and diagnosis of induction motors is presented. For this purpose the process is decomposed in several subprocesses.