Aiming at the problem of continuous model updating for fault recognition in the time-varying process, a novel methodcalled the Procrustes analysis–based Fisher discriminant analysis was proposed. First, each class of the training data waspreprocessed by Procrustes analysis. Second, the new test data were aligned with each class of the training data byProcrustes analysis. Then, all the data were reduced to a low-dimensional space using Fisher discriminant analysis. Finally,the Euclidean distance between the test data and the training data after the Procrustes analysis was calculated, and theclass recognition was achieved based on the discriminant principle of Fisher discriminant analysis. Two case studies showthat the proposed Procrustes analysis–based Fisher discriminant analysis is superior to the traditional method based onFisher discriminant analysis, and it can be used for fault recognition in a new and efficient way.