In this paper, an application, based on data from a popular dataset, shows in an empirical form the strengths and weaknesses of fuzzy ARTMAP neural networks as predictor of corporate bankruptcy. This is an advantageous approach enabling fast learning, self-determination of the network structure and high prediction accuracy. Experiments showed that the fuzzy ARTMAP outperforms statistical techniques and the most popular backpropagation MLP neural networks, all applied to the same dataset. An exhaustive search procedure over the Altman's financial ratios leads to the conclusion that two of them are enough to obtain the highest prediction accuracy. The experiments also showed that the model is not sensitive to outliers of the dataset. Our research is the first to use fuzzy ARTMAP neural networks for bankruptcy prediction.