This paper proposes a novel texture descriptor based indices of degrees of local approximating polynomials. An input image is divided into non-overlapping patches which are reshaped into a one-dimensional source vectors. These vectors are approximated using local polynomial functions of various degrees. For each element of the source vector, these approximations are ranked according to the difference between the original and approximated values. A set of indices of polynomial degrees form a local feature. This procedure is repeated for every pixel. Finally, a proposed texture descriptor is obtained from the frequency histogram of all obtained local features. A nearest neighbor classifier utilizing distance metric is used to evaluate a performance of the introduced descriptor on the following datasets: Brodatz, KTH-TIPS, KTH-TIPS2b, UCLA and Columbia-Utrecht (CUReT) with respect to different methods of texture analysis and classification. A proper parameter setup of the proposed texture descriptor is discussed. The results of this comparison demonstrate that the proposed method is competitive with the recent statistical approaches such as local binary patterns (LBP), local ternary patterns, completed LBP, Weber’s local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). At the same time, on KTH-TIPS2-b and KTH-TIPS datasets, the proposed method is slightly inferior to some of the state-of-the-art methods.