To achieve high recognition performance for a wide variety of noise and for a wide range of signal-to-noise ratios, this paper presents the integration of four noise reduction algorithms: spectral subtraction with smoothing of time direction, temporal domain SVD-based speech enhancement, GMM-based speech estimation and KLT-based comb-filtering. Recognition results on the Aurora2 task show that the effectiveness of these algorithms and their combinations strongly depends on noise conditions, and excessive noise reduction tends to degrade recognition performance in multicondition training.