A clustering algorithm for analyzing and partitioning the color images of natural scenes is described. The proposed method operates in the 1976 CIE (L*,a*,b*) uniform color coordinate system. It detects image clusters in some circular-cylindrical decision elements of the color space. This estimates the clusters' color distributions without imposing any constraints on their forms. Surfaces of the decision elements are formed with constant lightness and constant chromaticity loci. Each surface is obtained using only 1-D histograms of the L*,H°,C* cylindrical coordinates of the image data or the extracted feature vector. The Fisher linear discriminant method is then used to project simultaneously the detected color clusters onto a line for 1-D thresholding. This permits utilization of all the color properties for segmentation, and inherently recognizes their respective cross correlation. In this respect, the proposed algorithm also differs from the multiple histogram-based thresholding schemes in that it generates more reliable gross segmentation results.