Aircrafts are complex systems that require permanent and precise monitoring and troubleshooting. The automation of these tasks is thus of a high importance. This paper presents an intelligent decision system for faults diagnosis of aircrafts. The system relies on decision trees, being easier to interpret, quicker to learn than other data-driven methods, and able to work even with missing pieces of information. The used C4.5 algorithm automatically “learns” the best decision tree by performing a search through the set of possible trees according to the available training data. And Principal Component Analysis (PCA) is used to decrease the input data’s dimension. Compared to other methods, the proposed one is more advantageous and some presented evaluations demonstrate its abilities. High correct faults detection rates and low missed detection and false alarm rates are obtained. Such a decision system is highly useful for engineering consulting services, accumulating the knowledge for the operational rules of diagnosis, and the design of new aircrafts.