In deriving the data-driven temporal filters for speech features, the Linear Discriminant Analysis (LDA) has been shown to be successful in improving the feature robustness [1,2,3]. In our previous works [4,5] it was shown that the criteria of Principal Component Analysis (PCA) and Minimum Classification Error (MCE) can also be used to obtain the data-driven temporal filters in improving the speech recognition performance. In this paper, we proposed to perform Cepstral Normalization before applying these temporal filters, and evaluated the effectiveness of these different data-driven temporal filters on the AURORA2 database. Test results showed very signifi- cant improvements for almost all different cases, specially when the training and testing environments are highly mismatched. Robust MFCC Feature Extraction Algorithm Using