The comprehensive messages of six types of pattern signals such as broadband waveforms,AE parameters,analog parameters were collected with linear location simultaneously.Features of signals were analyzed for wave form,AE parameters,FFT spectrum and wavelet transform,in which 1300 samples were selected.The dimensional reduction of feature space was brought out by algorithm of multi-resolution wavelet transform,in which 5 features were extracted from each sample.An intelligent pattern classifier with B-P neural network was used in studying the learnt effect of network and the recognition ability for unknown flaw.Experiments showed that the results of recognition were satisfactory when the wavelet spectrum was taken as a sample feature vector.The average recognition accuracy of the six types of flaws was about 90.4%,and the best recognition accuracy amounted to 97.2%.The method has been applied to describing the entire three-point flexural failure process successfully for 90° and 0° specimens with no notch and 0° notched specimens.