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A new method for on-line induction motor fault detection is presented in this paper. This system utilizes a a c i a l neural networks to learn the spectral characteristics of a g d motor operating on-line. This learned spectrum may contain many harmonics due to the load which correspond to n o m d operating conditions. In order to reduce the number of harmonics which are continuously monitored to a manageable number, a selective frequency filter is employed. This frequency filter only passes those harmonics which are known to be of importance in fault detection, or which are continuously above a set level, to a neural net clustering algorithm. After a sufficient training period, the neural network signals a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, on-line failure prediction is possible with this system without requiring information on the motor or load characteristics. The detection algorithm was implemented and its performance verified on various fault types.