Coal ore isn't a uniform material. In order to optimize the coal liberation process it is necessary to classify a coal ore sample into its constituent components as quickly and cheaply possible. This paper investigates whether it is feasible to employ image processing and pattern recognition to segment a photographic image of coal ore into its various mineral components prior to the sample being crushed. The key to solving this classification problem is to model the visual texture of the various coal components by means of a low-dimensional texture space consisting of two main dimensions, namely: roughness and regularity. The regularity of each texture is estimated by means of a novel model-based approach. The distribution of the various coal components in the resultant feature space is modelled by means of a mixtures model and a simple nearest-neighbour decision rule is used to classify each pixel in the image. The performance of the classification system is encouraging and shows the feasibility of our idea.