A large number of applications have shown that the self-organizing map is a prominent unsupervised neural network model for high-dimensional data analysis. However, the high execution times required to train the map put a limit to its use in many application domains, where either very large datasets are encountered and/or interactive response times are required. In order to provide interactive response times during data analysis we developed the parSOM, a software-based parallel implementation of the self-organizing map Parallel execution reduces the training time to a large degree, with an even higher speedup obtained by using the resulting cache effects. We demonstrate the scalability of the parSOM system and the speed-up obtained on different architectures using an example from high-dimensional text data classification.