A feature selection methodology based on a novel Bhattacharyya space is presented and illustrated with a texture segmentation problem. The Bhattacharyya space is constructed from the Bhattacharyya distances of different measurements extracted with sub-band filters from training samples. The marginal distributions of the Bhattacharyya space present a sequence of the most discriminant sub-bands that can be used as a path for a wrapper algorithm. When this feature selection is used with a multiresolution classification algorithm on a standard set of texture mosaics, it produces the lowest misclassification errors reported.