The invention discloses a porcelain insulator vibrational acoustics test data sorting technique based on a back propagation (BP) neural network. The process comprises the steps of designing a BP neural network, taking at least one typical data of each class from the three classes of porcelain insulator vibrational acoustics test data after the noise reduction, generating feature vector as the input vector and conducting training to the BP neural network and then obtaining the trained neural network, and the test input vector is generated by the porcelain insulator vibrational acoustics test data, meanwhile conducting fast fourier transformation (FFT) to the porcelain insulator vibrational acoustics test data to generate a spectrogram and identify the spectrogram according to the test data file name, and then inputting the test input vector in the trained neural network, and therefore the neural network output is a certain of insulator defect form class codes; storing the generated spectrogram in the folder corresponding to the defect form code. The porcelain insulator vibrational acoustics test data sorting technique based on the BP neural network can classify the porcelain insulator vibrational acoustics test data rapidly without manual intervention and is high in classification effectiveness.