  We use a multi-color classification method introduced by Wolf, Meisenheimer &
Roeser (2000) to reliably identify stars, galaxies and quasars in the up to
16-dimensional color space provided by the filter set of the Calar Alto Deep
Imaging Survey (CADIS). The samples of stars, galaxies and quasars obtained
this way have been used for dedicated studies published in separate papers. The
classification is good enough to detect quasars rather completely and
efficiently without confirmative spectroscopy. The multi-color redshifts are
accurate enough for most statistical applications, e.g. evolutionary studies of
the galaxy luminosity function. We characterize our current dataset on the
CADIS 1h-, 9h- and 16h-fields. Using Monte-Carlo simulations we model the
classification performance expected for CADIS. We present a summary of the
classification results and discuss unclassified objects. More than 99% of the
whole catalog sample at R<22 (more than 95% at R<23) are successfully
classified matching the expectations derived from the simulations. A small
number of peculiar objects challenging the classification are discussed in
detail. Spectroscopic observations are used to check the reliability of the
multi-color classification (6 mistakes among 151 objects with R<24). We also
determine the accuracy of the multi-color redshifts which are rather good for
galaxies (sigma_z = 0.03) and useful for quasars. We find the classification
performance derived from the simulations to compare well with results from the
real survey. Finally, we locate areas for potential improvement of the
classification.
