Abstract In this paper a number of approaches to multispectral image segmentation and classification are considered. The methods range from the simple Bayesian decision rule for classification of image data on pixel-by-pixel basis, to sophisticated algorithms using contextual information. Both the spatial pixel category dependencies and the two-dimensional correlation-type contextual information have been incorporated in decision-making schemes. The aim of these algorithms is to achieve a greater reliability in the process of interpretation of remote-sensing data.