Local graylevel dependencies of natural images can be modeled by means of cooccurrence matrices containing joint probabilities of graylevel pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures, we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale cooccurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed based on texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of graylevel pairs, the impact of filters on the texture is described and used for classification of natural textures. This novel method was found to improve classification rates of the common cooccurrence matrix approach on standard textures significantly.