The ability to reason with information from a variety of sources is critical to the development of intelligent autonomous systems. Multisensor integration, or sensor fusion, is an area of research that attempts to provide a computational framework in which such perceptual reasoning can quickly and effectively be applied, enabling autonomous systems to function in unstructured, unconstrained environments. In this paper, the fundamental characteristics of the sensor fusion problem are explored. An hierarchical sensor fusion software architecture is presented as a computational framework in which information from complementary sensors is effectively combined. The concept of a sensor fusion pyramid is introduced, along with three unique computational abstractions: virtual sensors, virtual effectors, and focus of attention processing. The computing requirements of this sensor fusion architecture are investigated, and the blackboard system model is proposed as a computational methodology on which to build a sensor fusion software architecture. Finally, the Butterfly Parallel Processor is presented as a computer architecture that provides the computational capabilities required to support these intelligent systems applications.