Computational methods are useful for medical diagnosis because they provide additional information that cannot be obtained by simple visual interpretation. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. The study and development of Probabilistic Neural Network (PNN), Linear Vector Quantization (LVQ) Neural Network and Back Propagation Neural Network (BPN) for classification of fatty and cirrhosis liver from Computerized Tomography (CT) abdominal images is reported in this work. Neural networks are supported by more conventional image processing operations in order to achieve the objective set. To evaluate the classifiers, Receiver Operating Characteristic (ROC) analysis is done and the results are also evaluated by the radiologists. Experimental results show that PNN is a good classifier, giving an accuracy of 95% by holdout method and giving an accuracy of 96% by 10 fold cross validation method for classifying fatty and cirrhosis liver using wavelet based statistical texture features.