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This paper describes the application of wavelet transform (WT) for detection of bearing damage from the vibration signal of the ' bearing. The wavelet transform approach enables instant to instant observation of the contribution of different frequeng components over the full spectrum from. Actually, wavelet transform acts as a 'mathematical microscope ' in which one can observe different parts of the signal by 'adjusting the focus'. A new technique combining the WT with neural network for detection and classification of ball bearing fault in a three phase, 3.75 kW induction motor is presented in this paper. The method is tested successfully for three faulty bearing conditions: crack in inner race, crack in outer race and defect in balls.