The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data-driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c-means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab-scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods.