Any attribute selection criterion may work well with some data, and may not work well with other data. This work try to solve this problem by developing a new system known as Multi-Criteria Decision Tree (MCDT) learning system, it allows the decision tree to be learned using a combination of three attribute selection criteria: Gain Ratio, Chi_ Square(X2), and Apriori, then the learned tree is pruned using Expected Error Pruning algorithm. The user utilizes a parameter to adjust the pruning process to control the level where the pruning process takes place. The predictive accuracies of the decision trees learned using each of the three attribute selection criteria are calculated and compared with the new approach for fourteen data sets. The obtained decision t r e e learned using one of the implemented attribute selection criteria is visualized to the user; the visualized decision tree can be relearned with more than one attribute selection criteria. The user can modify the decision tree at any node by selecting a different attribute selection criterion, and reconstructing the sub-tree branches from the selected node. This process can be repeated any number of times for each node.