In this paper a hierarchical classification framework using the feature-weighting tree for the objective of applying diverse weighting to acoustic features is proposed for speech recognition. The hierarchical feature-weighting tree with a flexible structure complexity can be constructed optimally with the optimal splitting for the recognition confusion graph. Based on the minimum classification error principle, the subset-dependent training and the multi-level recognition method are proposed, where the feature weighting can be automatically trained without normalization in recognition. Both the mathematical analysis and the experimental results show that such a supervised hierarchical classification tree based on the feature weighting is efficient to reduce the speech recognition error.