In this paper we present two approaches to deal with degradation of automatic speech recognizers due to acoustic mismatch in training and testing environments. The first approach is based on the multi-band approach to automatic speech recognition (ASR). This approach is shown to be inherently robust to frequency selective degradation. In the second approach, we present a conceptually simple unsupervised feature adaptation technique, based on recursive estimation of means and variances of the cepstral parameters to compensate for the noise effects. Both techniques yield significant reduction in error rates.