Considering that the deviation between normal and abnormal status captured by each independent component (IC) is different from each other, a statistical analysis-driven approach by integrating kernel density estimation (KDE) with weighted independent component analysis (KDE-WICA) is developed. In KDE-WICA, KDE is used to estimate the probability and evaluate the importance of each IC, and subsequently set different weighting values on the ICs to highlight the deviation information for process monitoring. To overcome drastic fluctuations in the monitoring result, given that the previous status is not considered in determining the current status, a statistical weighting strategy is proposed to comprehensively evaluate the status of the process within a moving window (KDE-DWICA) and further improve the monitoring performance. KDE-DWICA is exemplified using a numerical study and the Tennessee-Eastman benchmark process. The monitoring results indicate that the performance of KDE-DWICA is superior to those of PCA, ICA, and other state-of-the-art variant-based methods.