  We present a methodology to discover outliers in catalogs of periodic
light-curves. We use cross-correlation as measure of ``similarity'' between two
individual light-curves and then classify light-curves with lowest average
``similarity'' as outliers. We performed the analysis on catalogs of variable
stars of known type from the MACHO and OGLE projects and established that our
method correctly identifies light-curves that do not belong to those catalogs
as outliers. We show how our method can scale to large datasets that will be
available in the near future such as those anticipated from Pan-STARRS and
LSST.
