In content-based image retrieval, a texture pattern may appear in a wide range of 3D views. Affine transformation is an approximation frequently used in practice to represent the variation of a pattern. The existing approaches to texture classification cannot cope with this variation. We use pattern regularity to define an affine-invariant feature vector and apply it to classification of structured textures under the orthographic projection, an important subset of affine transformations. The proposed approach achieves 85% accuracy for 18 patterns.