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Textures within real images vary in brightness, contrast, scale, and skew as imaging conditions change. To enable recognition of textures in real images, it is necessary to employ a similarity measure that is invariant to these properties. Furthermore, since textures often appear on undulating surfaces, such invariances must necessarily be local rather than global. Despite these requirements, it is only relatively recently that texture recognition algorithms with local scale and affine invariance properties have begun to be reported. Typically, they comprise detecting feature points followed by geometric normalization prior to description. We describe a method based on invariant combinations of linear filters. Unlike previous methods, we introduce a novel family of filters, which provides scale invariance, resulting in a texture description invariant to local changes in orientation, contrast, and scale and robust to local skew. Significantly, the family of filters enables local scale invariants to be defined without using a scale selection principle or a large number of filters. A texture discrimination method based on the chi2 similarity measure applied to histograms derived from our filter responses outperforms existing methods for retrieval and classification results for both the Brodatz textures and the University of Illinois, Urbana-Champaign (UIUC) database, which has been designed to require local invariance.