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One ob/eetive of the aerial radiometric surveys flown as part of the U.S. Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the spatial distribution of near-surface radioelement abundances on a regional scale. Some method for identifying groups of observations with similar ~-ray spectral signatures and radioelement concentration values was therefore required. It is shown in this paper that cluster analysis can identify such groups with or without a priori knowledge of the geology of an area. An approach that combines principal components analysis with convergent k-means cluster analysis is used to classify 6991 observations (eaeh observation comprising three radiometric variables) ]'rom the Precambrian rocks of the Copper Mountain, Wyoming area, This method is compared with a convergent k-means analysis that utitizes available geologic knowledge. Both methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and the fourth represents outliers {anoma-lously high 214Bi). A segmentation of the data corresponding to "geologic reality" as inter. preted by other methods has been achieved by perceptive quantitative analysis of aeriaI radiometric data. The techniques employed are composites of classical elustering methods designed to handle the special problems presented by large data sets.