Electric power utilities develop asset management (AM) strategies, based upon their reliability studies, which provide brighter images of the utility company's performance. Failure rate models are highly instrumental in transforming from simple reliability analyses into effective AM strategies. This paper proposes an adaptive network-based inference approach, to model momentary failure rate, in terms of the most influential factors. These factors are achieved through the vast field studies and historical outage fault statistics of the Greater Tehran Electricity Distribution Company (GTEDC) network.Performance of the presented failure rate model is tested, and discussions on capabilities of the model are then presented in detail.Index Terms-Power distribution systems, failure rate modeling, fault statistics, asset management (AM), reliability evaluation, fuzzy systems, adaptive network-based fuzzy inference systems (ANFIS), data mining, feature selection, utility management automation (UMA).