  We present a pattern-recognition based approach to the problem of removal of
polarized fringes from spectro-polarimetric data. We demonstrate that 2D
Principal Component Analysis can be trained on a given spectro-polarimetric map
in order to identify and isolate fringe structures from the spectra. This
allows us in principle to reconstruct the data without the fringe component,
providing an effective and clean solution to the problem. The results presented
in this paper point in the direction of revising the way that science and
calibration data should be planned for a typical spectro-polarimetric observing
run.
