In this study, we propose a maximum a posterior (MAP) estimation of channel bias to compensate the channel mismatch in telephone speech recognition. For a telephone speech, the channel bias is estimated by maximizing a posterior probability. Because a posterior probability is composed of a likelihood function and a prior density, we introduce a scale factor to evaluate their weights in MAP estimation. To further improve the performance, a prior channel statistics is extended to multiple components and the channel mismatch is separately compensated for different segments. Besides, a rapid MAP estimation applied in feature domain is also proposed for reducing the computational complexity. Experiments show that proposed method can significantly improve recognition rates and computational complexity.