In this study, we present the visualization and clustering capabilities of self-organizing maps (SOM) for analyzing high dimensional data. We used SOM because they implement an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. We used surface texture parameters of volcanic ash that arose from different fragmentation mechanisms as input data. We found that SOM cluster 13-dimensional data more accurately than conventional statistical classifiers. The component planes constructed by SOM are more successful than statistical tests in determining the distinctive parameters. (c) 2007 Elsevier Ltd. All rights reserved.