The partial discharges (PD) measurement approach in power equipments is a very important inspection technique for insulation deterioration assessment. The PD based approach possesses the greatest potential for further development. This study proposes a noncontact type acoustic measurement system. We first investigate an acoustic measurement method in the laboratory. To prove the accuracy of the acoustic measurements, we proceed with, in the laboratory, signal-pattern comparison between the acoustic measurement method and the pulse current method. This study creates polar-coordinate and discharge type identification patterns. We propose the use of the q-£p-t patterns, the polar-coordinate patterns and discharge type identification patterns, with mutual cross-reference, to identify the discharge type. Then this study applies the wavelet transform to suppress noises; a wavelet mother function most similar to the acoustic PD signals is chosen and then set the filtering threshold value for the wavelet transform. The signals' features will be extracted after the noises are eliminated. The experimental results indicate that the application of wavelet transform can effectively eliminate the field noises. Next, the features will be used to build the training database for the back-propagation neural network (BNN) to construct the discharge patterns' recognition and identification system. Finally, we apply the finished neural networks to field signal-pattern identification. The proposed acoustic measurement system is applied on line to epoxy-resin transformers, power distributors, and the like. The superior measurement results we obtained shall be able to correctly identify power equipment's PD fault types.