Slow feature analysis (SFA) has been extensively adopted for process monitoring. Since the prominent ability of exploring dynamic information of the industrial process, SFA could monitor the process static and dynamic deviations concurrently. However, for complex and large-scale processes, it is difficult for a single SFA model to monitor the whole process well because of the complex relationship within massive volumes of variables. To address this issue and get a better monitoring performance, a novel ensemble process monitoring method based on slow feature analysis models is proposed as ensemble SFA (ESFA) in this paper. The proposed method develops a set of SFA models based on different combinations of variables, and the divisive hierarchical clustering algorithm (DHCA) is performed to pick out some models with great diversity as the base learners. Then, the fault detection results of base models would be combined into a comprehensive indicator through Bayesian inference. Furthermore, the ESFA method also provides an ES2 statistic for monitoring process dynamics to differentiate the deviations of normal operating condition changes from dynamic anomalies incurred by real faults. Finally, compared with basic SFA and several principal component analysis (PCA)-based methods, the validity of the proposed method is demonstrated through the case studies of the Tennessee Eastman (TE) benchmark process and the BSM1 process.