Abstract This paper describes a totally automatic non-parametric clustering algorithm and its application to unsupervised image segmentation. The clusters are found by mode analysis of the multidimensional histogram of the considered vectors through a non-iterative peak-climbing approach. Systematic methods for automatic selection of an appropriate histogram cell size are developed and discussed. The algorithm is easily parallelizable and is simulated on a SEQUENT parallel computer. Image segmentation is performed by clustering features extracted from small local areas of the image. Segmentation of textured, color, and gray-level images are considered. Eight-dimensional random field model based features, three-dimensional RGB components, and one-dimensional gray levels are utilized for these three types of images respectively. For texture segmentation, an image plane cluster validity procedure based on region growing of the mapped back clusters in the feature space is developed. Most of the phases are also parallelized resulting in almost linear speed ups. Quite satisfactory results are obtained in all cases.