In this paper, we analyze the results of the recent Aurora large vocabulary evaluations. Two consortia submitted proposals on speech recognition front ends for this evaluation: (1) Qualcomm, ICSI, and OGI (QIO), and (2) Motorola, France Telecom, and Alcatel (MFA). These front ends used a variety of noise reduction techniques including discriminative transforms, feature normalization, voice activity detection, and blind equalization. Participants used a common speech recognition engine to postprocess their features. In this paper, we show that the results of this evaluation were not significantly impacted by suboptimal recognition system parameter settings. Without any front end specific tuning, the MFA front end outperforms the QIO front end by 9.6% relative. With tuning, the relative performance gap increases to 15.8%. Both the mismatched microphone and additive noise evaluation conditions resulted in a significant degradation in performance for both front ends.