  In this paper we describe the use of a new artificial neural network, called
the difference boosting neural network (DBNN), for automated classification
problems in astronomical data analysis. We illustrate the capabilities of the
network by applying it to star galaxy classification using recently released,
deep imaging data. We have compared our results with classification made by the
widely used Source Extractor (SExtractor) package. We show that while the
performance of the DBNN in star-galaxy classification is comparable to that of
SExtractor, it has the advantage of significantly higher speed and flexibility
during training as well as classification.
