Readmission is a major source of cost for healthcare systems. Hospital-specific readmission rates are considered an indicator of hospital performance and generate public interest regarding the health care quality. We aimed to identify those patients who are likely to be readmitted to the hospital. The identified patients can then be considered by health care personnel for application of preventive alternative measures such as: providing intensive post-discharge care, managing the conditions of the most vulnerable in their home, supporting self-care, and integrating health services and information technology systems to avoid unnecessary readmissions. Neural Network, Classification and Regression model and Chi-squared Automatic Interaction Detection models were used for the readmission prediction. All models were able to perform with an overall accuracy above 80%, with the latter two models having the advantage of providing the user with the opportunity of selecting different misclassification costs. We employed C5.0 algorithm to search for recurring pattern in the history or demographics of patients who have been readmitted and explored if a rule of thumb can be derived to predict those at risk of future readmissions. Moreover, the key variables influencing readmission were studied based on a large data set. The most important factors contributing to readmission were determined such as age, sex, number of previous prescriptions and length of previous stays, place of service, and number of previous claims.