In this paper, we obtained the data, which is required in training the neural network and diagnosing the degradation degree, by introducing the AE detection that is effective method in ordinary degradation diagnosis on activation. Aa the results of generalization tests by appling neural network to the unknown AE patterns obtained from two kinds of specimen, firstly as to evaluate an objective performance of neural network, the recognition ration for no-void specimen and 1[mm] -void specimen are appeared to be 98.9% and 92.5%, respectively. Also, in the evaluation of the adaptability of neural network with a new type of 0.2[mm] -void specimen, it is confirmed that the result appears to be 64% of recognition ratio at 94% of confidence interval coefficient in expectation output 0.2. On the other hand, the recognition capability of the neural network was confirmed by data from no-void and 1[mm] void specimen. The results prove the promising possibility of the application of ANN to discriminate specific void affecting as main degradation source at partial discharge condition in insulator containing multi-void by accummulated data base.