Connectionist models have had problems representing and applying general knowledge rules that specifically require variables. This variable binding problem has barred them from performing the high-level inferencing necessary for planning, reasoning, and natural language understanding. This paper describes ROBIN, a structured neural network model capable of high-level inferencing requiring variable bindings and rule application. Variable bindings are handled by signatures—activation patterns which uniquely identify the concept bound to a role. Signatures allow multiple role-bindings to be propagated across the network in parallel for rule application and dynamic inference path instantiation. Signatures are integrated within a connectionist semantic network structure whose constraint-relaxation process selects between those newly-instantiated inferences. This allows ROBIN to handle an area of high-level inferencing difficult even for symbolic models, that of resolving multiple constraints from context to select the best interpretation from among several alternative and possibly ambiguous inference paths.