In this article, three different methods for hybridization and specialization of real-time recurrent learning (RTRL)-based neural networks (NNs) are presented. The first approach consists of combining recurrent networks with feedforward networks. The second approach continues with the combination of multiple recurrent NNs. The last approach introduces the combination of connectionist systems with instructionist artificial intelligence techniques. Two examples are added to demonstrate properties and advantages of these techniques. The first example is a process diagnosis task where a hybrid NN is connected to a knowledge-based system. The second example is a NN consisting of different recurrent modules that is used to handle missing sensor data in a process modelling task.