  We describe an Artificial Neural Network (ANN) approach to classification of
galaxy images and spectra. ANNs can replicate the classification of galaxy
images by a human expert to the same degree of agreement as that between two
human experts, to within 2 T-type units. Similar methods are applied to
classification of galaxy spectra. In particular, Principal Component Analysis
of galaxy spectra can be used to compress the data, to suppress noise and to
provide input to the ANNs. These and other classification methods will soon be
applied to the Anglo-Australian 2-degree-Field (2dF) redshift survey of 250,000
galaxies.
