  The results of morphological galaxy classifications performed by humans and
by automated methods are compared. In particular, a comparison is made between
the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey
(SDSS) commissioning data (Shimasaku et al. 2001) with those of supervised
artificial neural network programs constructed using the MATLAB Neural Network
Toolbox package. Networks in this package have not previously been used for
galaxy classification. It is found that simple neural networks are able to
improve on the results of linear classifiers, giving correlation coefficients
of the order of 0.8 +/- 0.1, compared with those of around 0.7 +/- 0.1 for
linear classifiers. The networks are trained using the resilient
backpropagation algorithm, which, to the author's knowledge, has not been
specifically used in the galaxy classification literature. The galaxy
parameters used and the network architecture are both important, and in
particular the galaxy concentration index, a measure of the concentration of
light towards the centre of the galaxy, is the most significant parameter.
Simple networks are briefly applied to 29,429 galaxies with redshifts from the
SDSS Early Data Release. They give an approximate ratio of types E/S0:Sp:Irr of
14 +/- 5 : 86 +/- 12 : 0 +/- 0.1, which broadly agrees with the well known
approximate ratios of 20:80:1 observed at low redshift.
