This paper introduces a new texture analysis method called texture feature coding method (TFCM) for classiยฎcation of ultrasonic liver images. The TFCM transforms a gray-level image into a feature image in which each pixel is represented by a texture feature number (TFN) coded by TFCM. The TFNs obtained are used to generate a TFN histogram and a TFN co-occurrence matrix (CM), which produces texture feature descriptors for classiยฎcation. Four conventional texture analysis methods that are gray-level CM, texture spectrum, statistical feature matrix and fractal dimension, are used also to classify liver sonography for comparison. The supervised maximum likelihood (ML) classiยฎers implemented by different type texture features are applied to discriminate ultrasonic liver images into three disease states that are normal liver, liver hepatitis and cirrhosis. The 30 liver sample images proven by needle biopsy are used to train the ML system that classify on a set of 90 test sample images. Experimental results show that the ML classiยฎer together with TFCM texture features outperforms one with the four conventional methods with respect to classiยฎcation accuracy.