- Abstract We present a technique for segmenting multi-dimensional data cubes based on multi-dimensional histograms. The histograms are formed from single gray-scale image reductions of the data cube such as principal component images. A segmentation is effected by associating each pixel with one of the peaks in the histogram. No spatial constraints are imposed and no training pixels are required. The following refinements to this simple process are described: proper weighting of the different principal components as a function of the peak shape; and automatic methods based on an entropy measure to generate a reasonable segmentation at a specified number of levels. Examples from both visible and infrared hyperspectral data will be shown.