The SOFM is trained using colour and Gabor texture values extracted from a set of training images. It is shown that the SOFM is capable of dividing the total input space into a small number of meaningful clusters. These are then used to index each pixel in the image. Similarly indexed pixels correspond to regions having similar colour and texture properties, and hence a segmentation is available. One of the papers main contributions is to quantify the success of a SOFM for this segmentation task on a large set of outdoor scenes, and not a small number of simple, artificially textured images. The high dimensionality of the input data is successfully reduced using this method in such a way as to allow segmentation. Practical considerations discussed include how the size of SOFM, the training regime required and the input dimensionality affect the segmentation quality.