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Multivariate statistical process monitoring (MSPM) is an efficient data-driven faultdetection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T2 of PLS is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Controllimits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further,the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional method. A case study on Tennessee Eastman process(TEP) indicates the efficiency of the proposed approach.