In this paper, we present evaluation results of a noise adaptive speech recognition system with combination of several techniques for robust speech recognition. The evaluation was on AURORA 3 database which contains noisy digit utterances collected in real car environments through close-talking and hands-free microphones. The techniques in the system include segmentation, maximum likelihood linear regression (MLLR) and non-stationary environment compensation by noise adaptive speech recognition. Through experiments, it is observed that the system has competitive performance improvement in all evaluations over the baseline results provided for the evaluation. As a whole, the system achieved 28% of relative performance improvement.