  Quantitative morphological classification of galaxies is important for
understanding the origin of type frequency and correlations with environment.
But galaxy morphological classification is still mainly done visually by
dedicated individuals, in the spirit of Hubble's original scheme, and its
modifications. 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 methods. Here we show results from the first systematic comparison of
the dispersion among human experts classifying a uniformly selected sample of
over 800 digitised galaxy images. These galaxy images were then classified by
six of the authors independently. The human classifications are compared with
each other, and with an automatic classification by Artificial Neural Networks
(ANN). It is shown that the ANNs can replicate the classification by a human
expert to the same degree of agreement as that between two human experts.
