Osteoporosis is an age-based disease causing skeletal disorder. It is described by the little bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy subjects and osteoporotic cases show a high grade of resemblance. This study presents an evaluation of osteoporosis identification using texture descriptor Local Binary Pattern (LBP) and Shift Local Binary Pattern (SLBP). In contrast with the conventional LBP, with the shifted LBP specific number of binary local codes are generated for each pixel place. The distinguishing ability of the texture descriptors is evaluated using ten-fold cross validation and leave-one out scheme using different machine learning techniques. The results prove the SLBP outperforms the traditional LBP for bone texture characterization.