Chinese sign language (CSL) is the language used by many deaf people in China. The signer expresses himself by gestures and hand motions. Therefore CSL recognition is a problem of dynamic continuous signal recognition. Most sign language recognition systems use HMMs(hidden Markov models) presently. But the numbers of basic gestures included in different words are variable, so the accuracy will be affected if all models consist of the same number of states. It is difficult to set the exact number of states manually. In this paper, an approach based on dynamic programming is proposed to estimate the number of the states, and a CSL training and recognition system are implemented based on HMMs consisting of different number of states. The result of the experiments shows that the speed and accuracy of CSL recognition are improved.%中国手语是中国聋人使用的语言，主要通过手势动作来表达一定的含义.因而，手语识别问题是动态连续信号的识别问题.目前大部分手语识别系统采用HMMs(hidden Markov models)作为系统的识别技术.由于各个词包含的基本手势数不同，若所有模型都由同样数目的状态结点构成会影响识别率.而由人为每个词设置状态数又很难达到完全准确，所述系统使用一种基于动态规划的估计状态结点数的办法，并实现了基于具有不同状态数目的HMM的训练及识别过程，实验结果表明，该系统在手语的识别速度和识别精度方面都有所提高.
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