In this paper a new monitoring algorithm utilizing a change in time-series distribution of process data is presented since the distribution reflects the corresponding operating condition. In order to quantitatively evaluate the difference between two data sets, a modified dissimilarity index is defined. It represents the degree of dissimilarity between data-sets. In training step the confidence interval of each eigenvalue is obtained from the data taken in normal operation. Then, current operating condition is monitored by checking whether dissimilarity index abruptly changes and whether each eigenvalue is contained within its confidence interval. This approach is used to identify various internal and external disturbances in the data from the simulated activated sludge process. Simulation results have clearly shown that the detection performance of the proposed method can detect the various faults and disturbances, and can automatically discriminate between serious and minor anomalies of faults. That is, it can detect not only the disturbances, but isolate the sources of them. These results confirm that the proposed method is a proper monitoring technique for the wastewater treatment process which has nonstationary property and various disturbances.