  We perform a comparison of different approaches to star-galaxy classification
using the broad-band photometric data from Year 1 of the Dark Energy Survey.
This is done by performing a wide range of tests with and without external
`truth' information, which can be ported to other similar datasets. We make a
broad evaluation of the performance of the classifiers in two science cases
with DES data that are most affected by this systematic effect: large-scale
structure and Milky Way studies. In general, even though the default
morphological classifiers used for DES Y1 cosmology studies are sufficient to
maintain a low level of systematic contamination from stellar
mis-classification, contamination can be reduced to the O(1%) level by using
multi-epoch and infrared information from external datasets. For Milky Way
studies the stellar sample can be augmented by ~20% for a given flux limit.
Reference catalogs used in this work will be made available upon publication.
