The multiple modeling strategy and Gaussian mixture model (GMM) have been widely used to monitor multimode processes. On the basis of a deterministic view, multiple modeling strategy builds the specific model for each mode, which can extract more accurate information for monitoring. However, multiple modeling strategy is unable to deal with the situation in which the online mode information cannot be determined, and this condition easily leads to a severe error when an inappropriate model is used for monitoring. GMM builds a mixture model for the whole process from a probabilistic view. It unites all the models probabilistically for monitoring without having to identify the mode information. However, it may perform badly for some specific modes because some irrelevant models of other modes are introduced by GMM. Besides, it may not efficiently capture the local features especially for complex processes with transitional modes. In this paper, a novel monitoring strategy, which combines the advantages of multiple modeling strategies and GMM, is proposed for multimode processes. All possible models are probabilistically united for monitoring when the mode cannot be identified for sure. If the mode can be determined completely, the corresponding model is deterministically used for monitoring. To evaluate the feasibility and efficiency of the proposed method, the Tennessee Eastman challenge is demonstrated to compare the proposed method with multi-PCA and traditional GMM.