The modified projection to latent structures (MPLS) algorithm is a multivariate statistical process monitoring method reflecting the relationship between process variable and quality variable, which is effectively applied to fault monitoring of the steady state process. For time-varying industrial systems, the MPLS model is difficult to describe the current process effectively, so it is necessary to update the model to enhance the monitoring of the current process. The commonly used model update method is a data expansion method. However, the repeated use of historical data in this method leads to the accumulation of modeling samples and the inefficiency of model updating. In order to improve the dynamic update efficiency of the model, this paper proposes a recursive-MPLS (RMPLS) algorithm, which uses the recursive structure to dynamically update the MPLS model with new data. Compared with the data expansion method,RMPLS avoids sample accumulation and greatly improves the model update efficiency. Finally, in Tennessee-Eastman process, comparing the model updating calculation and the quality-related fault monitoring effect of the RMPLS and the MPLS, the results show that the RMPLS effectively reduces the model updating calculation, and improves the quality-related fault monitoring capability.