Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspective, this strategy has the advantage that all the original information in the hyperspectral data can be retained while addressing the curse of dimensionality given by the Hughes phenomenon. Even if SP prior to classification has been widely used, the strategies adopted to perform such partitioning did not consider the diversity of spectral classes in the scene. In other words, available techniques are not driven by the information contained in the classes of interest, which can be very useful to perform the SP in a more effective manner for classification purposes. To address this issue, in this paper, we present a new class-oriented SP technique that exploits prior information about the classes by automatically ranking the spectral bands that are more useful for each specific class (instead of considering the hyperspectral image as a whole). The resulting multiple subgroups of bands with lower dimensionality are then fed to a multiple classifier system. Our experimental results, conducted with three different hyperspectral airborne images, suggest that the presented method leads to competitive results when compared to other state-of-the-art approaches in the field.