The change point detection of time series is widely applied in various fields. In some applications, a minimum period is required before a state change. Motivated by such applications, a constrained Hidden Markov Model, which combines with the shortest state continuous length constraint, is proposed in this study. Moreover, a constrained Baum-Welch training algorithm and a constrained Viterbi state extraction algorithm are also given. And experimental results based on the simulation data and GNP data sets indicate that the constrained HMM has higher performance than the general HMM.