This paper is about the development of an expert system for automatic classification of granite tiles through computer vision. We discuss issues and possible solutions related to image acquisition, robustness against noise factors, extraction of visual features and classification, with particular focus on the last two. In the experiments we compare the performance of different visual features and classifiers over a set of 12 granite classes. The results show that classification based on colour and texture features is highly effective and outperforms previous methods based on textural features alone. As for the classifiers, SVM shows to be superior to the others, provided that the governing parameters are tuned properly.