With many clustering algorithms available, it may be difficult to discern which is better for a given task. This study compares the performance of two clustering algorithms, the Bayesian classifier AutoClass and a Kohonen map, for the task of identifying classes of different textures in images based on statistics derived from gray-level co-occurrence matrices. The performance of the two algorithms is assessed in terms of quality of the classification. Comparisons of quality are given in terms of objective criteria such as cluster diameter, intercluster distance, etc. as well as subjective judgements by domain experts. Two different types of images are used. The first type of image consists of standard texture images in which textures classes are readily identified by novices. The second type consists of side-scanned sonar images in which the clusters are not necessarily apparent to novices and are not always classified consistently by domain experts (geologists). INTRODUCT..