Artificial immune system is a new artificial intelligence methodology that is increasingly attracting much attention for monitoring engineered systems. In an artificial immune system (AIS), principles and processes of the natural immune system are abstracted and applied in solving real world problems. One immune-inspired principle is negative selection, whereby the natural immune system distinguishes between the body's own (self) cells and foreign (non-self) cells. In this paper, we apply this principle for process monitoring and fault diagnosis. In the proposed approach, samples from a given state (such as normal or known fault) are considered as self. The proposed approach uses these samples to develop a description of the non-self-space in the form of a collection of spherical detectors. This representation is in contrast to traditional statistical and pattern recognition algorithms that store descriptions of the space occupied by the normal samples. The proposed fault detection and identification (FDI) approach is a generic one and can be applied for monitoring and fault diagnosis of both continuous as well as batch processes and transient operations since it does not require that the underlying data stems originate from a specified statistical distribution. The effectiveness of the proposed approach for monitoring and fault diagnosis is demonstrated through various case studies. The results of the case studies clearly illustrate the method's ability to provide excellent monitoring and diagnosis performances with (i) complete fault coverage (all the faults studied can be readily detected and identified), (ii) very high overall recognition rate, (ii) low false positive rate, (iii) high true positive rate, and (iv) early fault detection and diagnosis. A comparison of performance with traditional principal component analysis (PCA) based approaches is also performed.