PURPOSE To assess the role of an automated Computer Aided Diagnosis (CAD) system in differentiation of Healthy Subjects to Chronic Liver Disease (CLD) Patients in terms of liver fibrosis (F0-F4), using Ultrasound Shear Wave Elastography (SWE). METHODS Clinical Dataset consisted of 125 subjects, 55 Healthy (F0) whom condition was validated with Normal Biochemical Markers and clear Clinical History, and 70 with CLD (F1-F4) whom condition was validated with Liver Biopsy. For each subject an SWE examination on their liver right lobe was performed using Supersonic Imagine's Aixplorer ultrasound system, and an SC6-1 transducer. The examination was performed using preset default settings for Abdomen/Liver, and according to literature's suggested guidelines. An SWE image was then acquired, and a separation of the colored areas from the grayscale ones was performed. An RGB-to-Stiffness algorithm was consequently applied on the resulting image (Stiffness Box), using the nbuilt-in colormap, to replace RGB values to Stiffness values. A segmentation procedure was employed using thresholds that segment the image to 5 stiffness range clusters (0-6, 6-12, 12-18, 18-24, and 24-30 kPa), and extract 7 features from each cluster (Mean, Median, Standard Deviation, Sample Kurtosis, 10th Percentile, 90th Percentile, and Cluster's Pixel Percentage on the Stiffness Box) deriving 35 features for each SWE image. The computed feature set was then fed to a designed SVM classifier. SVM-model evaluation was performed by means of exhaustive search, and leave-one-out methods. RESULTS Maximum classification accuracy (85.6%) in distinguishing Healthy from chronic liver disease patients was obtained employing three features (Blue mean and Cyan median values, and Red maximum pixel number) with sensitivity and specificity values of 91.9% and 79.3% respectively. CONCLUSION The proposed CAD system accurately differentiates Healthy to CLD patients assisting in evaluating important factors for performing SWE examination, enriching its guidelines, and aiding clinicians in a more accurate diagnosis.