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This paper presents an effective approach for rotating machine fault diagnosis, based on quantitative wavelet selection and improved local discriminate bases. Mutual information entropy is utilized as a quantitative measure to select the most suitable base wavelet for wavelet packet transform. Then an improved local discriminate bases method based on the normalized energy difference and relative entropy is used to choose the optimal set of orthogonal wavelet subspaces. After that, two optimal sets of orthogonal subspaces from wavelet packet decomposition have been obtained and the energy features extracted from the subspaces appearing in both sets will be selected as input to the support vector machine (SVM) to diagnose the fault states of rotating machines. Experiment study conducted from a rolling bearing test setup has verified the effectiveness of the proposed method for machine defect severity evaluation. 