  Preprocessing the input patterns seems the simplest approach to invariant
pattern recognition by neural networks. The Fourier transform has been proposed
as an appropriate and elegant preprocessor. Nevertheless, we show in this work
that the performance of this kind of preprocessor is strongly affected by the
number of stored informations. This is because the phase of the Fourier
transform plays a more important role than the amplitude in the recognition
process.
