Abstract: Every mode has different features in a multimode process,so the local features of modal data can be more effectively than global features for the reasonable characterization of chemical process.In order to use the local characteristics of multimodal data,this paper proposes a local feature based multiple model method,called,Local Feature-based Multiple Model (LFMM),for process monitoring.Firstly,the sequential information between data and the modal data features is utilized in the offline phase and the coefficient of variance of data in different time windows is applied for the clustering of the training data of multimode process.In the latter model phase,LOF algorithm is utilized to compute the local data density in their mode data set.In the online phase,by taking the local data density as statistic character,a new global probability index is established as a monitoring statistic for multimode process monitoring.Finally,TE process is adopted to verify the effectiveness of the proposed method.