Simple structure and robust property of a reservoir neural network are preferable for hardware implementation of a high performance learning system, especially for time-series data processing. One of salient feature of the reservoir network is reproducibility or consistency of its responses to the same or similar inputs. This is usually guaranteed through the echo state property of the network by properly choosing synaptic weights among reservoir neurons. Another important feature is a variety of dynamics in the reservoir, which makes the reservoir to process complex time-varying input signals. One way to increase the variety of dynamics is introducing chaotic behavior by destabilize the reservoir network by changing weight values. However, this will violate the echo state property, therefore, chaotic dynamics are usually avoided in the reservoir computing.In this paper, we propose a method to introduce high-dimensional chaotic dynamics into the reservoir network, but keeping its consistency. To achieve this, we use a chaotic neural network model in the reservoir network, while keeping the weight matrix in the reservoir network to satisfy the echo state property criteria. In order to show the consistency of the chaotic neural network reservoir, preliminary results for chaotic time-series predictions through the chaotic neural network reservoir are illustrated. In addition, we discuss the application of the chaotic neural network reservoir to a self-aware hardware system.