The energy content of each frequency sub-band of the acoustic emission(AE) signal after decomposition is related to the type of the roller bearing defect, AE signals measured from the bearing test rig were decomposed into a number of frequency sub-bands by using harmonic wavelet packet, and energy features associated with each sub-band were selected. The energy features were then used as inputs to a back-propagation neural network classifiers for identifing the bearing’s fault. In the bearing fault recognition, harmonic wavelet packet was compared with daubechies wavelet packet. The experimental results indicate that the proposed fault diagnosis method is effective and can be used for roller bearing fault recognition.