The integration of different learning and adaptation techniques in one architecture, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Most of these approaches, however, follow an ad-hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains often difficult to compare the various systems conceptually and evalua te their performance comparatively. This paper is first aimed at classifying state of the art intelligent systems, which have evolved over the past decade in the soft computing community. We identify four categories, based on the systems' overall architecture: (1) single component systems, (2) fusion based systems, (3) hierarchical systems, and (4) hybrid systems. Next the paper introduces introduce a unifying paradigm, derived from concepts well known in the AI and agent community, as conceptual framework to better understand, modularize, compare and evaluate the individual approaches. It is crucial for the design of intelligent systems to focus on the integration and interaction of different learning techniques in one model rather then merging them to create ever new techniques. Finally, in this paper an original instantiation of a framework of a fault location hybrid approach based on knowledge and model methods is presented and discussed. The performance of the hybrid approach is evaluated by compare its advantages and disadvantages to the individual approaches applied to the problem of fault location in power distribution systems. As final comment, the location of faults on subtransmission and distribution systems has started receiving some attention as many utilities are operating in a deregulated environment and are competing with each other to increase the availability and quality of power supply to the customers.