Induction motor is the most widely used rotating machinery in industries. The primary objective of the present work is to detect the most widely occurring faults, for example faults on rolling element bearings and inter-turn faults in the stator windings of an induction motor, at an early stage to take corrective action prior to the catastrophic failure. This work deals with diagnosis of both electrical (stator inter-turn) and mechanical (bearing) faults in induction motor using wavelet analysis. An artificial neural network (ANN) was developed to use this data for diagnosis purpose. Thus, it is demonstrated that ANN with wavelet coefficients data, can successfully detect electrical and mechanical faults.