This paper describes an architecture for the control of robotic devices, and in particular of anthropomorphic hands, characterized by a hierarchical structure in which every level of the architecture contains data and control function with varying degree of abstraction. Bottom levels of the hierarchy interface directly with sensors and actuators, and process raw data and motor commands. Higher levels perform more symbolic types of tasks, such as application of boolean rules and general planning operations. The implementation of the layer has to be consistent with the type of operation and its requirements for real time control. In the paper we present one implementation of the rule level with a Boolean Artificial Neural Network which would have a response time sufficient for producing reflex corrective action at the actuator level.