The objective of this paper is to develop the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are (1) a MDA-assisted neural network, (2) an ID3-assisted neural network, and (3) a SOFM(self organizing feature map)-assisted neural network. Both the MDA-assisted neural network and the ID3-assisted neural network are the neural network models operating with the input variables selected by the MDA method and IDS respectively. The SOFM-assisted neural network combines a backpropagation model (supervised learning) with a SOFM model (unsupervised learning). The performance of the hybrid neural network model is evaluated using MDA and IDS as a benchmark. Empirical results using Korean bankruptcy data show that hybrid neural network models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.