We present a system for named entity recognition (ner) in astronomy journal articles. We have developed this system on a ne corpus comprising approximately 200,000 words of text from astronomy articles. These have been manually annotated with ∼40 entity types of interest to astronomers. We report on the challenges involved in extracting the corpus, defining entity classes and annotating scientific text. We investigate which features of an existing state-of-the-art Maximum Entropy approach perform well on astronomy text. Our system achieves an F-score of 87.8%. 