Neural networks have been used in several domains and applications in our life. One of the main challenges is the hardware implementation of neural networks. The hardware design is fixed in the number of nodes and layers that make the network is applied to specific applications. This paper presents a reconfigurable neural network where the number of layers and nodes can be changed according to the applications. The proposed method is based on Network-on-Chip (NoC) which is used for routing data between layers and nodes. Each router, in NoC, is connected to m nodes that can represent a part or complete layer. According to the reconfiguration, the number of routers can be selected to present the number of layers, and the number of nodes per layer is decided by the needed nodes in each router. The proposed method is implemented on FPGA Altera 10 GX, and it achieves an accuracy of 97% for using the MNIST dataset. The throughput and delay of the proposed method have efficient results compared to the traditional method.