One of the primary concerns in most financial institutions to have an appropriate method for ranking customers. Bank customers are the primary sources of creating income and the success of banking industry depends on how to select good customers for allocation of loans. This paper uses Decision Tree, K-nearest neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and Logistic Regression for data categorization to estimate credit ranking of bank customers in one of major banks in Middle East. The results indicate that Logistic Regression was considered as the best method for ranking customers with the precision of 76.17% while Decision Tree was considered as the weakest technique with the precision of 73.30%.