In an automobile, the brake is an essential part responsible for the control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the safety of the vehicle cum passenger. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is necessitated. Vibration-based condition monitoring techniques are gaining momentum. This study is one such attempt to categorize the faults that occur in a hydraulic brake system through vibration analysis. In this research, the performance of an artificial intelligence technique called Artificial Immune Recognition System for brake fault diagnosis has been reported. A hydraulic brake system test setup was fabricated. The vibration signals under good and faulty conditions of a brake system were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signals. The best features were identified using an attribute evaluator. The selected features were then classified using various versions of the Artificial Immune Recognition System (AIRS). The classification accuracy of such artificial intelligence techniques has been reported and discussed. © 2020 IEEE.