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The binary tree, quadtree, and octree decomposition techniques are widely used in computer graphics and image processing problems. Here, the techniques are reexamined for pattern recognition and shape analysis applications. It has been shown that the quadtree and octree techniques can be used to find the shape hull of a set of points in space while their n-dimensional generalization can be used for divisive hierarchical clustering. Similarly, an n-dimensional binary tree decomposition of feature space can be used for efficient pattern classifier design. Illustrative examples are presented to show the usefulness and efficiency of these hierarchical decomposition techniques.