  Rare extragalactic objects can carry substantial information about the past,
present, and future universe. Given the size of astronomical databases in the
information era it can be assumed that very many outlier galaxies are included
in existing and future astronomical databases. However, manual search for these
objects is impractical due to the required labor, and therefore the ability to
detect such objects largely depends on computer algorithms. This paper
describes an unsupervised machine learning algorithm for automatic detection of
outlier galaxy images, and its application to several Hubble Space Telescope
fields. The algorithm does not require training, and therefore is not dependent
on the preparation of clean training sets. The application of the algorithm to
a large collection of galaxies detected a variety of outlier galaxy images. The
algorithm is not perfect in the sense that not all objects detected by the
algorithm are indeed considered outliers, but it reduces the dataset by two
orders of magnitude to allow practical manual identification. The catalogue
contains 147 objects that would be very difficult to identify without using
automation.
