Texture analysis is one of the important as well as useful tasks in image processing applications. Man y texture models have been developed over the past few years and Local Binary Patterns (LBP) is one of the simpl e and efficient approach among them. A number of extensions to the LBP method have been also presented but t he problem remains challenging in feature vector gener ation and comparison. As textures are oriented and scaled differently, a texture model should effectively han dle grey-scale variation, rotation variation, illum ination variation and noise. The length of the feature vect or in a texture model also plays an important role in deciding the time complexity of the texture analysis. This s tudy proposes a new texture model, called Optimized Local Ternary Patterns (OLTP) in the spatial methods of t exture analysis. The proposed texture model is base d on Local Ternary Patterns (LTP), which in turn is based on L BP. A new concept called “Level of Optimality” to select the optimal set of patterns is discussed in this study. This proposed texture model uses only optimal patt erns to extract the textural information from the digital images an d thereby reducing the length of the feature vector . This proposed model is robust to image rotation, grey-sc ale transformation, histogram equalization and nois e. The results are compared with other widely used texture models by applying classification tests to variety of texture images from the standard Brodatz texture database. Experimental results prove that the proposed textur e model is robust to grey-scale variation, image rotation, his togram equalization and noise. Experimental results also show that the proposed texture model improves the classi fication accuracy and the speed of the classificati on process. In all tested tasks, the proposed method outperform s the earlier methods.