  Supervised artificial neural networks are used to predict useful properties
of galaxies in the Sloan Digital Sky Survey, in this instance morphological
classifications, spectral types and redshifts. By giving the trained networks
unseen data, it is found that correlations between predicted and actual
properties are around 0.9 with rms errors of order ten per cent. Thus, given a
representative training set, these properties may be reliably estimated for
galaxies in the survey for which there are no spectra and without human
intervention.
