Abstract: In this article a speaker-dependent,topic-constrained and continuous Chinese speech recognition system based on vector quantization (VQ),hidden Markov models (HMM's) and finite states syntactic analysis is studied.The difference of zero-crossing amplitudes was used as one of parameters to determine the position of voice start and end.The words-data used in HMM's training was obtained by machine segmentation of continuous spoken sentences.Multipossible transitions of models nodes were considere in each frame.So the recognition accuracy was improved.The system can be applied in speaker-dependent and topic constrained information request.