Variable-weighted Fisher discriminant analysis (VW-FDA) is proposed to improve the fault diagnosis performance of the conventional FDA. VW-FDA incorporates the variable weighting into FDA. The variable weighting is used to find out each weight vector for all faults. After all fault data are weighted by the corresponding weight vectors, the summed fault data can be constructed to magnify each fault's local characteristics. Then, VW-FDA is performed on the summed fault data rather than the original fault data. It is helpful to extract discriminative features from overlapping fault data. Moreover, the partial F-values with the cumulative percent variation are used for exactly variable weighting, which is indispensable to VW-FDA. The proposed approach is applied to Tennessee Eastman process. The results demonstrate that VW-FDA shows better fault diagnosis performance than the conventional FDA. (C) 2009 Published by Elsevier Ltd.