  Digital synoptic sky surveys pose several new object classification
challenges. In surveys where real-time detection and classification of
transient events is a science driver, there is a need for an effective
elimination of instrument-related artifacts which can masquerade as transient
sources in the detection pipeline, e.g., unremoved large cosmic rays,
saturation trails, reflections, crosstalk artifacts, etc. We have implemented
such an Artifact Filter, using a supervised neural network, for the real-time
processing pipeline in the Palomar-Quest (PQ) survey. After the training phase,
for each object it takes as input a set of measured morphological parameters
and returns the probability of it being a real object. Despite the relatively
low number of training cases for many kinds of artifacts, the overall artifact
classification rate is around 90%, with no genuine transients misclassified
during our real-time scans. Another question is how to assign an optimal
star-galaxy classification in a multi-pass survey, where seeing and other
conditions change between different epochs, potentially producing inconsistent
classifications for the same object. We have implemented a star/galaxy
multipass classifier that makes use of external and a priori knowledge to find
the optimal classification from the individually derived ones. Both these
techniques can be applied to other, similar surveys and data sets.
