Environmental robustness and speaker independence are import issues of current speech recognition research. Channel and speaker adaptation methods do the best job when the adaption is done towards a normalized acoustic model. Normalization methods might make use of the model but primarily inuence the signal such that important information is kept and unwanted distortions are cancelled out. Most large vocabulary conversational speech recognition systems use Cepstral Mean Subtraction (CMS), a channel normalization approach to compensate for the acoustic channel (and also the speaker). In this paper we discuss the basic algorithm and variations of it in the context of conversational speech and report our experience using different approaches on two widely used conversational speech recognition tasks.