Abstract Liver Cirrhosis is a very popular disease in Taiwan. Utilizing ultrasound to diagnose Cirrhosis is one of the most frequently used methods by the doctors, but patients have different physiology structure and doctors also have some different subjective judgments, therefore contrived mistake is increased in diagnosis probability. This research is to apply wavelet analysis to classify two cases between Cirrhosis and normal liver. In this research, I firstly introduce a summary to wavelet analysis, and secondly by means of wavelet analysis to calculate texture energy to proceed texture characterization in a supersonic diagnostic set to get ultrasound images. Finally, to use Multivariate Statistical Methods to effectively classify two kinds of different ultrasound liver images, furthermore to quantify the texture energy to distinguish Cirrhosis and normal liver. This classified method is an assistance for doctors to diagnose Cirrhosis, so as to reduce man-made mistake in diagnosis probability.