This paper presents a Bayesian approach to learning for HMMs in speech recognition. The implementation of Bayesian learning for HMMs in speech recognition is discussed, including the requirement of maintaining the original HMM constraints, choice of prior and utterance recognition. This work shows that the Bayesian learning approach can be successfully applied to complex models when the amount of training data is small.