Many strategies have been developed for the allocation of image pixels, or picture elements, to classes in order to obtain segmentation of images. Difficulties are encountered when a pixel may be assigned to more than one class according to the information gleaned from training sets. The following question arises: is there real overlap or are the training sets spectrally discrete? A general algorithm is presented which quantifies the degree of overlap of classes defined by training sets drawn from an image. Test results show that age classes within a forest plantation are spectrally discrete, even though poor classification accuracies were obtained using conventional classifiers.