In this study, we propose a theoretical general architecture for a decision making system. This architecture is domain independent and based on three main operations. The first operation is obtaining the decision tree from the given datasets. The second operation is injecting the extracted rules from the decision tree into a neural network using an algorithm which we call semi-KBANN algorithm and the final operation is using a heuristic function technique to help in making the final decision of a certain situation. The architecture mechanism is based on feeding each stage operation from the output of the previous stage. The operations are executed serially, stage 1, stage 2 and then stage 3. In stage 1, we extract classification rules from the induced decision trees. In the second stage, we incorporate the extracted rules into a neural network to be able to have more generalization that might be tied by decision trees and finally we use heuristic mechanism to help the system to take a proper decision. We assume that the dataset is cleaned from the outliers using any of the many well known algorithms. Outliers is beyond our study.