Kernel principal component analysis (KPCA) based monitoring has good fault detection capability for nonlinear process systems; however, it can only isolate variables that have a contribution to the occurrence of a fault, and thus it is not precise in diagnosing. Since there is a cause and effect relationship between different variables in a process, accordingly a network-based causality analysis method was developed for different fault scenarios to show causal relationships between different variables and to see the causal effect between the variables most contributing to the occurrence of a fault. It was shown that KPCA in combination with causality analysis is a powerful tool for diagnosing the root cause of a fault in the process. In this paper the proposed methodology was applied to a fluid catalytic cracking (FCC) unit and the Tennessee Eastman process to diagnose root causes for different faulty scenarios.