Essentially all work in connectionist learning up to now has been induction from examples, but instruction is as important in symbolic artificial intelligence as it is in nature. This paper describes an implemented connectionist learning system that transforms an instruction expressed in a description language into an input for a connectionist knowledge representation system, which in turn changes the network in order to integrate new knowledge. Integration is always important when a single new fact causes changes in several parts of the knowledge-base; it is an adjustment which cannot easily be done with learning-by-example techniques only. The new, integrated knowledge can be used in conjunction with prior knowledge. The learning method used is recruitment learning, a technique which converts network units from a pool of free units into units which carry meaningful information, i.e represent generic concepts.