The human immune system provides rich metaphors for adaptive pattern recognition. Fault detection and diagnosis in chemical processes is commonly formu- lated as a pattern recognition problem. However, conven- tional methods for fault diagnosis often do not have a mechanism to adapt and learn as the process changes over time. In this paper, we propose an Artificial Immune Sys- tem (AIS) framework that endows learning to statistical process monitoring techniques such as Principal compo- nent analysis. The proposed AIS framework also provides a direct means to incorporate recovery actions after a failure has been detected and diagnosed. We demonstrate the efficacy of the proposed framework using a simulated binary distillation column case study.