In this paper, we investigate material classiﬁcation from single images obtained under unknown viewpoint and illumination. It is demonstrated that materials can be classiﬁed using the joint distribution of intensity values over extremely compact neighbourhoods (starting from as small as 3×3 pixels square), and that this can outperform classiﬁcation using ﬁlter banks with large support. It is also shown that the performance of ﬁlter banks is inferior to that of image patches with equivalent neighbourhoods. We develop novel texton based representations which are suited to modelling this joint neighbour hood distribution for MRFs. The representations are learnt from training images, and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. Three such representations are proposed, and their performance is assessed and compared to that of ﬁlter banks. The power of the method is demonstrated by classifying 2806 images of all 61 materials present in the Columbia-Utrecht database. The classiﬁcation performance surpasses that of recent state of the art ﬁlter bank based classiﬁers such as Leung and Malik (IJCV 01), Cula and Dana (IJCV 04), and Varma and Zisserman (IJCV 05). We also benchmark performance by classifying all the textures present in the UIUC, Microsoft Textile and the San Francisco outdoor datasets. We conclude with discussions on why features based on compact neighbourhoods can correctly discriminate between textures with large global structure and why the performance of ﬁlter banks is not superior to that of the source image patches from which they were derived.