  We employ classical statistical methods of multivariate classification for
the exploitation of the stellar content of the Hamburg/ESO objective prism
survey (HES). In a simulation study we investigate the precision of a
three-dimensional classification (Teff, log g, [Fe/H]) achievable in the HES
for stars in the effective temperature range 5200<Teff<6800K, using Bayes
classification. The accuracy in temperature determination is better than 400K
for HES spectra with S/N>10 (typically corresponding to B_J<16.5). The
accuracies in log g and [Fe/H] are better than 0.68dex in the same S/N range.
These precisions allow for a very efficient selection of metal-poor stars in
the HES. We present a minimum cost rule for compilation of complete samples of
objects of a given class, and a rejection rule for identification of corrupted
or peculiar spectra. The algorithms we present are being used for the
identification of other interesting objects in the HES data base as well, and
they are applicable to other existing and future large data sets, such as those
to be compiled by the DIVA and GAIA missions.
