A discrete-HMM training algorithm which has strong ability of pattern classification is presented in this paper. By VQ (vector quantization) technique, this algorithm trains data from all speakers in mixed mode to generate the speaker characteristic pattern, which includes features of all speakers. By substituting the VQ code\|book in conventional discrete-HMM with characteristic pattern, the ability of pattern classification for observation symbol sequence is enhanced, therefore the classifying ability of discrete-HMM is improved. The experimental results show that the algorithm can improve the system's recognition performance, and reduce the dependence extent of HMM on the scale of training set. Moreover, the calculation quantum of this algorithm in recognition stage is obviously less than that of conventional HMM training algorithm, therefore it has higher practical value.