There is currently no generally-accepted theory explaining how neural systems realise complex function. Indeed, it is believed by some that neural systems are fundamentally opaque. A framework of hierarchy is proposed as the basis of neural theory. By the application of hierarchy to neural systems it is possible to explain how complex function is computed. At the primitive (hardware) level it is only possible to understand the computation of primitive functions. To understand the computation of higher level function it is necessary to abstract primitive function, via an arbitrary number of intermediate levels of complexity, to the appropriate level of abstraction. Application of the framework is facilitated by a software tool which implements a specification as a neural system, to which training can then be applied. This specification is hierarchical, and is described in a fully distributed, object-oriented style. Networks constructed by this method are not restricted to any of the traditional neural models. The class of topologies which may be implemented is unrestricted. The framework is applied to the recognition of numberplates. This practical demonstration shows that (a) hierarchy enables neural computation of complex function to be understood; (b) the application of hierarchy allows the integration of specification and learning as methods of implementation; and (c) the framework facilitates the scaling-up of neural systems.