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Automatic speech recognition (ASR) model adaptation is important to many real-life ASR applications due to the variability of speech. The differences of speaker, bandwidth, context, channel and et al. between speech databases of initial ASR models and application data can be major obstacles to the effectiveness of ASR models. ASR models, therefore, need to be adapted to the application environments. Maximum Likelihood Linear Regression (MLLR) is a popular model-based method mainly used for speaker adaptation. This paper proposes a feature-based statistical Data Mapping (SDM) approach, which is more flexible than MLLR in various applications, such as different bandwidth and context. Experimental results on the TIMIT database show that ASR models adapted by the SDM approach have improved accuracy.