Abstract This paper presents a set of new fault detection and isolation (FDI) approaches based on a Modified Gath-Geva (MGG) Clustering approach. The proposed approaches are formulated in the forms of a combined principal component analysis (PCA)-MGG or nonlinear PCA (NPCA)-MGG schemes. The PCA and NPCA perform as a data preprocessing step on the measured industrial multivariate time series data to enhance their informative richness, capsulated in a more compact form. Then, the MGG clustering approach is used to detect and isolate the faults by organizing the PCA/NPCA-transformed data in different clusters. A distributed-MGG scheme is also proposed in this paper as a new FDI approach which is based on a distributed monitoring configuration. The main idea is to divide the overall monitoring task into a series of local distributed monitoring sub-tasks so as to track and capture the process faults locally. The diagnostic performances of the proposed FDI approaches are evaluated by a set of comparative test studies on the Tennessee Eastman process plant as a large-scale benchmark problem.