In this paper, we present a theoretically and computationally simple but efficient approach for rotation invariant texture classification. This method is based on new texture signatures extracted from spectrum. Rotation invariant texture features are obtained based on the extension of the derived signatures. The features are tested with 1000 randomly rotated samples of 20 Brodatz texture classes. Comparative study results show that our method is highly efficient in rotation invariant texture classification.