Abstract: In this paper, a cascade connection hidden Markov model （CCHMM） method is proposed. This model allows state transition, skip and duration, and extends the way of HMM pattern description of handwritten English words. According to the statistic syntax, it may depict the behavior of handwriting curve more reliably, while character segmenting and labeling are unneeded. The Viterbi algorithm for the cascade connection model may be applied after the whole sample series of a word is input. Compared to the method of creating models for each word in lexicon, this method could avoid the problem of recognition asynchronous. Experiments on EW-1 database shows that CCHMM approach could obtain 89. 26% recognition rate for the first candidate. The proposed approach cuts 39. 18% error rate of ligature model method, whose first candidate is 82. 34%.