Abstract: A great deal of effort is being made to increase accuracy and reliability of Condition Based Maintenance systems; for instance, by improved feature selection strategies or optimization approaches of classifier parameters. In this work a novel classification methodology is presented, covering from the characterization of the acquired physical magnitudes to the configuration of the classification algorithms. The proposed methodology provides a more accurate classification structure by identifying and isolating conflictive regions in the classification space and by specialized feature reduction and classification stages for them. The proposed Hierarchical Classification Scheme is composed by sequential layers, in which the clear membership regions are identified first, and the conflictive regions of classification are tackled in upper levels. Such treatment of the conflictive regions is based on new feature space transformation to provide an optimized data understanding and, then, better chances of classification. Improving classification with this method compared to other alternatives implies the avoidance of over-fitting the classification training. Also, the proposed methodology, due to its hierarchical structure nature, offers the opportunity to configure the feature reduction and classification algorithms to obtain the optimal data management.