We proposed an efficient method for classification of diffused liver diseases based on Gabor wavelet. It is well known that Gabor wavelets attain maximum joint spacefrequency resolution which is highly significant in the process of texture extraction and presentation. This property has been explored here as the proposed method outperforms the classification rate obtained by using dyadic wavelets and methods based on statistical properties of textures. The feature vector is relatively small compared to other methods. This has a significant impact on the speed of retrieval process. In addition, the proposed algorithm is not sensitive to shift of the image contents. Since shifting the contents of an image will cause a circular shift of the Gabor filter coefficients in each sub-band. The proposed algorithm applied to discriminate ultrasonic liver images into three disease states that are normal liver, liver hepatitis and cirrhosis. In our experiment 45 liver sample images from each three disease states which already proven by needle biopsy were used. We achieved the sensitivity 85% in the distinction between normal and hepatitis liver images and 86% in the distinction between normal and cirrhosis liver images. Based on our experiments, the Gabor wavelet is more appropriate than dyadic wavelets and statistical based methods for texture classification as it leads to higher classification accuracy.