Abstract: To improve the prediction performance of soft sensor models and to reduce the influence on product quality caused by various challenges including process nonlinearity, multiple operating phases, and different local dynamics, we propose a multi-model soft sensor method based on the just-in-time learning (JITL) method. The proposed method uses the Gaussian mixture model (GMM) to distinguish the data from different operating phases and applies an adaptive JITL strategy to update the built models. The relation between input and output data is modeled using the Gaussian process regression (GPR) model. Whenever a new sample is available, local GRP models are constructed using a portion of the most relevant samples selected by the Euclidean distance and angle method in the different operational phases. Then, according to the posterior probabilities of the sample belonging to the different operational phases, the predictions of the local GPR models are combined to obtain the desired global output. Compared with traditional soft sensors based on a single model, the JITL-based approach exhibits a more flexible structure and the process dynamics can be captured better. A Tennessee Eastman (TE) chemical process is used to demonstrate the feasibility and effectiveness of the proposed approach. The results show that the proposed approach provides higher predictive accuracy and better generation ability.