In the current ASR systems the presence of competing speakers greatly degrades the recognition performance. This phenomenon is getting even more prominent in the case of hands-free, far-field ASR systems like the “Smart-TV” systems, where reverberation and non-stationary noise pose additional challenges. Furthermore, speakers are, most often, not standing still while speaking. To address these issues, we propose a cascaded system that includes Time Differences of Arrival estimation, multi-channel Wiener Filtering, nonnegative matrix factorization (NMF), multi-condition training, and robust feature extraction, whereas each of them additively improves the overall performance. The final cascaded system presents an average of 50% and 45% relative improvement in ASR word accuracy for the CHiME 2011 (non-stationary noise) and CHiME 2012 (non-stationary noise plus speaker head movement) tasks, respectively.