A new approach to overall multivariate statistical monitoring, including all static normal operations and the intermediate states between them, is introduced and then applied to real plant data. Principal component analysis or partial least squares (PLS) is used to reduce the dimensionality of data and to remove collinearity. After the compression of data, the credibilistic fuzzy c-means method is used to appropriately group the data. These algorithms use score vectors of PLS as feature vectors. So, we find total proper different normal operation conditions. To identify operation change modes, a discrimination index is proposed based on the time-series pattern of the membership values of clusters. Using this index, we can monitor all data patterns. The proposed monitoring method is applied to real experimental data from a full-scale power plant process and simulated data from a continuous stirred tank reactor model, and the results are discussed. In particular, the proposed method is found to easily discriminate between intermediate states and faults (or abnormalities) occurring within the process data.