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Environmental robustness is an important area of research in speech recognition. Mismatch between trained speech models and actual speech to be recognized is due to factors like background noise. It can cause severe degradation in the accuracy of recognizers which are based on commonly used features like mel-frequency cepstral co-efficient (MFCC) and linear predictive coding (LPC). It is well understood that all previous auditory based feature extraction methods perform extremely well in terms of robustness due to the dominantfrequency information present in them. But these methods suffer from high computational cost. Another method called sub-band spectral centroid histograms (SSCH) integrates dominant-frequency information with sub-band power information. This method is based on sub-band spectral centroids (SSC) which are closely related to spectral peaks for both clean and noisy speech. Since SSC can be computed efficiently from short-term speech power spectrum estimate, SSCH method is quite robust to background additive noise at a lower computational cost. It has been noted that MFCC method outperforms SSCH method in the case of clean speech. However in the case of speech with additive noise, MFCC method degrades substantially. In this paper, both MFCC and SSCH feature extraction have been implemented in Carnegie Melon University (CMU) Sphinx 4.0 and trained and tested on AN4 database for clean and noisy speech. Finally, a robust speech recognizer which automatically employs either MFCC or SSCH feature extraction methods based on the variance of shortterm power of the input utterance is suggested.