One well-known and effective method used for computationally efficient texture classification is the use of statistical information on 3×3 pixel blocks such as local binary patterns (LBP). However, there has been negligible research on sizes of pixel blocks beyond 3×3 while using the histogram approach. Specifically, larger or non-square features might give better classification results. Our proposed method constructs features, with arbitrary size and shape, that will give the best results for classifying a specific texture, and still keeping the feature vectors as short as possible. In this work, we show the selected features and the performance of our method with a minimum distance classifier and with a neural network and provide quantitative comparisons to the 3×3 block method on both the Brodatz and Ponce texture databases.