  We investigate the application of 2-dimensional Principal Component Analysis
(2D PCA) to the problem of removal of polarization fringes from
spectro-polarimetric data sets. We show how the transformation of the PCA basis
through a series of carefully chosen rotations allows to confine polarization
fringes (and other stationary instrumental effects) to a reduced set of basis
"vectors", which at the same time are largely devoid of the spectral signal
from the observed target. It is possible to devise algorithms for the
determination of the optimal series of rotations of the PCA basis, thus opening
the possibility of automating the procedure of de-fringing of
spectro-polarimetric data sets. We compare the performance of the proposed
method with the more traditional Fourier filtering of Stokes spectra.
