Neural networks (NNs) can solve only a simple problem if the network size is too compact, on the other hand, if the network size increases, it costs a lot in terms of calculation time. So, we have studied how to construct the network structure with high performances and low costs in space and time. A solution is a multi-branch structure. Conventional NNs uses the single-branch for the connections, while the multi-branch structure has multi-branches between the nodes. In this paper, a new method which enable the multi-branch NNs to have localized property is proposed. It is well known that RBF networks have localized property that makes it possible to approximate functions faster than signioidal NNs. By using the multi-branch structure having localized property, NNs could obtain high performances keeping the lower costs in space and time. Simulation results of function approximations and classification problems illustrated the effectiveness of multi-branch NNs.