In this paper we study the parallelization of the inference process for connectionist models. We use a symbolic formalism for the representation of the connectionist models. With this translation, the training mechanism is local in the elements of the network, the computing power is improved in the network nodes and a local hybridization with symbolic parts is achieved. The inference in the final knowledge network can be parallelized, whether the knowledge corresponds to a symbolic module, a connectionist model or a hybrid connectionist-symbolic module. Besides, the concurrency for knowledge networks corresponding to connectionist models is presented for the phases of processing and training. The parallelization is studied for a multiprocessor architecture with shared memory.