Texture classi cation is an important rst step in image segmentation and image recognition. In this report we present the Detrending Moving Average (DMA) algorithm as a robust and informative classi cation algorithm. We train the DMA algorithm with the images of the UIUC dataset. The Cholensky-Levinson Factorization algorithm is used to generate arti cial fractal surfaces as a reference dataset. In the classi cation results the DMA algorithm seems to be able to detect scale, aspect and rotation changes in the analysed random textures.