The mismatch between the system training and operating conditions often has negative influences on the automatic speech recognition (ASR) systems. Noise in the operating environments is commonly encountered. ASR model adaptation is an important way to enhance the system performance in noisy environments. This paper proposes a feature-based statistical data mapping (SDM) approach for robust noisy digit recognition. The recognition tasks are carried out on the AURORA 2 database. Compared to other model adaptation methods such as MLLR, the SDM approach has more robust performances.