The submitted system for CHiME-5 challenge focuses on implementing a better front-end for an automatic speech recognition (ASR) system trained on the data provided by CHiME-5. In this work, we focus on using non-negative matrix factorization (NMF) based technique to denoise and dereverberation. In Approach 1, the degraded single-channel speech utterances were enhanced using multi-channel Weighted prediction error (WPE) or NMF followed by a minimum variance distortionless response (MVDR) beamformer to obtain an enhanced signal. In Approach 2, we used multi-channel MVDR followed by a NMF based single-channel enhancement. Using the baseline acoustic model (AM), these enhanced speech utterances did not provide improved WER compared to the baseline Beamformit based system. So, we retrained the AM using WPE enhanced data for training (Approach 3). These approaches were able to improve the ASR results as compared to baseline. We are submitting results for the single-array track and only focus on acoustic robustness (i.e., ranking A).