The original ultrasonic flaw signals are decomposed into a finite number of stationary intrinsic mode functions(IMF) by empirical mode decomposition(EMD)

and then a set of eigenvalues are obtained in time domain and frequency domain from the IMF components.The signal eigenvector is constructed by the eigenvalues for identification.BP neural network is used as diagnosis decision-making classifier.In neural network model

the input node corresponds to the signal eigenvector for identification and the output node corresponds to the flaw type.The basic theory and the course of implementing of this method are discussed in this paper.The experimental results by typical artificial flaw echo signals show that the method has better performance in detecting such flaw signals.