  We study the asymptotic behavior of wavelet coefficients of random processes
with long memory. These processes may be stationary or not and are obtained as
the output of non--linear filter with Gaussian input. The wavelet coefficients
that appear in the limit are random, typically non--Gaussian and belong to a
Wiener chaos. They can be interpreted as wavelet coefficients of a generalized
self-similar process.
