  The rapid increase in data on galaxy images at low and high redshift calls
for re-examination of the classification schemes and for new automatic
objective methods. Here we present a classification method by Artificial Neural
Networks. We also show results from a comparative study we carried out using a
new sample of 830 APM digitised galaxy images. These galaxy images were
classified by 6 experts independently. It is shown that the ANNs can replicate
the classification by a human expert almost to the same degree of agreement as
that between two human experts, to within 2 $T$-type units. Similar methods can
be applied to automatic classification of galaxy spectra. We illustrate it by
Principal Component Analysis of galaxy spectra, and discuss future large
surveys.
