An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares (MKPLS) was presented. It is known that conventional batch process monitoring methods, such as multiway partial least squares (MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares (KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process. And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.