Power distribution systems have been significantly affected by many events. Effective outage cause identification can help expedite the restoration procedure and improve the system reliability. Fuzzy classification E-algorithm and biological immune system based AIRS algorithm have demonstrated good capability in outage cause identification especially with imbalanced data. E-algorithm can produce inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, Fuzzy Artificial Immune Recognition System (FAIRS) has been proposed to utilize the advantages of both E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major causes (tree, animal, and lightning) as prototypes. It is compared with both E-algorithm and AIRS, and the results show that FAIRS achieves comparable performance while being able to extract linguistic rules with rule length flexibility to explain the inference with significantly reduced computing time than E-algorithm.