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This paper addresses the problem of discriminative wavelet packet (WP) filter bank selection for pattern recognition. The problem is formulated as a complexity regularized optimization criterion, where the tree-indexed structure of the WP bases is explored to find conditions for reducing this criterion to a type of minimum cost tree pruning, a method well understood in regression and classification trees (CART). For estimating the conditional mutual information, adopted to compute the fidelity criterion of the minimum cost tree-prnning problem, a nonparametric approach based on product adaptive partitions is proposed, extending the Darbellay-Vajda data-dependent partition algorithm. Finally, experimental evaluation within an automatic speech recognition (ASR) task shows that proposed solutions for the WP decomposition problem are consistent with well understood empirically determined acoustic features, and the derived feature representations yield competitive performances with respect to standard feature extraction techniques.