This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature