In this paper a method of continuous speech recognition based on hidden Markov models （HMM） and vector quantization （VQ） is discussed. According to the method

forming its own HMM for each word’s voice

searching the optimal path of state transition network combined by HMM’ s of all words of the vocabulary

it has been realized to recog-nize continuous spoken sentences without presegmentation of each word. The recognition performance is improved by using finite-state syntactic analysis and other techniques. The de-monstration system with vocabulary of 150 words can process 18 type of English sentences. The delay from speech end to getting recognition result is nearly 0.62 times as long as the duration of speech. Tested by 312 sentences including 2710 words

the word recognition accuracy is 97.3% when the average speech speed is 2.32 words per second.