This paper addresses the issue of fault diagnosis in the heat exchanger of an aircraft Air Conditioning System (ACS). The heat exchanger cools the air by transferring the heat to the ram-air. Due to a variety of biological, mechanical and chemical reasons, the heat exchanger may experience fouling conditions that reduces the efficiency and could considerably affect the functionality of the ACS. Since, the access to the heat exchanger is limited and time consuming, it is preferable to implement an early fault diagnosis technique that would facilitate Condition Based Maintenance (CBM). The main contribution of the paper is pre-flight fault assessment of the heat exchanger using a combined model-based and data-driven approach of fault diagnosis. A Simulink model of the ACS, that has been designed and validated by an industry partner, has been used for generation of sensor data for various fouling conditions. A total of nine different fouling levels are simulated including the nominal condition. Subsequently, the output temperature data of the heat exchanger is analyzed using the Principal Component Analysis (PCA) method for feature extraction. The Support Vector Machine (SVM) and the k-Nearest Neighbor (k-NN) methods are used for data classification into different faulty conditions. The results are evaluated by cross-validation and presented in terms of the confusion matrix, Correct Classification Rate (CCR), sensitivity, and specificity.