  We apply the technique of self-organising maps (Kohonen 1990) to the
automated classification of singly periodic astronomical lightcurves. We find
that our maps readily distinguish between lightcurve types in both synthetic
and real datasets, and that the resulting maps do not depend sensitively on the
chosen learning parameters. Automated data analysis techniques are likely to be
become increasingly important as the size of astronomical datasets continues to
increase, particularly with the advent of ultra-wide-field survey telescopes
such as WASP, RAPTOR and ASAS.
