This paper presents a data-driven method based on principal component analysis and Fisher discriminant analysis to detect and diagnose multiple faults including fixed bias, drifting bias, complete failure of sensors, air damper stuck and water valve stuck occurred in the air handling units. Multi-level strategies are developed to improve the diagnosis efficiency. Firstly, system-level PCA model I based on energy balance is used to detect the abnormity in view of system. Then the local-level PCA model A and B based on supply air temperature and outdoor air flow rate control loops are used to further detect the occurrence of faults and pre-diagnose them into various locations. As a linear dimensionality reduction technique, moreover, Fisher discriminant analysis is presented to diagnose the fault source after pre-diagnosis. With Fisher transformation, all of the data classes including normal and faulty operation can be re-arrayed in a transformed data space and as a result separated. Comparing the Mahalanobis distances (MDs) of all the candidates, the least one can be identified as the fault source. (c) 2008 Elsevier Ltd. All rights reserved.