The use of Logical operators as a new tool for Texture Classification problem is presented. Logical operators like Hadamard, Adding, Arithmetic, Conjunction, Disjunction and Equivalence are used to form a set of filters. The order-2 matrix of each operator is used for filtering the gray scaled textured image. Standard deviation matrices are computed from these filtered images over a 5 by 5 moving window. Features are extracted on this standard deviation matrix using zonal masks. Zonal masks with annular-ring, wedge, and parallel slit sampling geometries are used. Supervised classifier such as Euclidean is used in the experiment. Experiments are performed with Brodatz textures and also on remote sensing images. Nine different sets of 6 Bradatz textures are used. Two remote sensing images are classified using pixel to pixel classification. Out of the 33 Brodatz textures taken for classification, for 22 textures the Percentage of Correct Classification (PCC) is 100%, for 4 textures PCC is in the range of 95% to 99%. The algorithm developed is also computationally efficient as it involves only addition operations.