  We discuss the application of a class of machine learning algorithms known as
decision trees to the process of galactic classification. In particular, we
explore the application of oblique decision trees induced with different
impurity measures to the problem of classifying galactic morphology data
provided by Storrie-Lombardi et al.(1992).
  Our results are compared to those obtained by a neural network classifier
created by Storrie-Lombardi et al, and we show that the two methodologies are
comparable. We conclude with a demonstration that the original data can be
easily classified into less well-defined categories.
