In the continuous density hidden Markov model for speech recognition, the number of mixture components in each state is usually fixed throughout all the states. The authors propose the use of a different number of mixture components for each state. For this purpose, a method is also proposed for determining the number of mixture components from the entropy information of each state. The recognition accuracy with the proposed algorithm improves considerably.