Kernel principal component analysis (KPCA) has been widely applied to nonlinear process monitoring. Conventionally, a single Gaussian kernel function with the width parameter determined empirically is selected to build a single KPCA model. Obviously, it is very blind to determine only a single Gaussian kernel function only by experience, especially when the fault information is unavailable. If a poor Gaussian kernel function is selected unfortunately, the detection performance may be degraded greatly. Furthermore, a single kernel function usually cannot be most effective for all faults, i.e., different faults may need different width parameters to maximize their respective monitoring performance. To address these issues, we try to improve the KPCA-based process monitoring method by incorporating the ensemble learning approach with Bayesian inference strategy. As a result, the monitoring performance is not only more robust to the width parameter selection but also significantly enhanced. This is validated by two case studies, a simple nonlinear process and the Tennessee Eastman benchmark process.