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Reverberant noise has been a major concern in speech recognition systems. Many speech recognition systems, even with state-of-art features, fail to respond to reverberant effects and the recognition rate deteriorates. This paper explores the significance of normalization strategies in reducing statistical mismatches for robust speech recognition in reverberant environment. Most normalization works focused only on ambient noise and have yet been experimented on reverberant noise. In addition, we propose a new approach for the odd order cepstral moment normalization which is computationally more efficient and reduces the convergence rate in the algorithm. The proposed method is experimentally justified and corroborated by the performance of other normalization schemes. The results emphasize the significance of reducing statistical mismatches in feature space for reverberant speech recognition.