KPLS is a very efficient technique for tackling complex nonlinear data sets by mapping an original input space into a high-dimensional feature space. However, KPLS may not function well when the model set is contaminated to a large extent by outliers. In this Article, a robust version of kernel partial least squares (KPLS) called spherical kernel partial least squares (SKPLS) is introduced for monitoring nonlinear processes. The key idea of SKPLS is to project all of the feature vectors in the feature space onto a unit sphere and then to perform KPLS on the sphered feature vectors. The effects of the outliers in the original data are eliminated or diminished because of the sphering. The robust monitoring statistics and robust control limits are derived for process monitoring purposes. The simulation results show that the proposed process monitoring strategy not only works well when the modeling data set does not contain outliers but also offers a satisfactory efficiency when the modeling data set is highly contaminated by outliers.