In this paper, a new neural network model called the hierarchical overlapped neural gas (HONG) network is introduced and its performance on several datasets is described. In order to obtain improved classification accuracy, the HONG network partitions the input space by projecting the input data onto several different second layer neural gas networks. This duplication enables the HONG network to generate multiple classifications for every sample presented in the form of confidence values, and these confidence values are combined to obtain the final classification. Excellent recognition rates for several benchmark datasets are presented.