  We present a study of the effect of component separation on the recovered
cosmic microwave background (CMB) temperature distribution, considering
Gaussian and non-Gaussian input CMB maps. First, we extract the CMB component
from simulated Planck data (in small patches of the sky) using the maximum
entropy method (MEM), Wiener filter (WF) and a method based on the subtraction
of foreground templates plus a linear combination of frequency channels (LCFC).
We then apply a wavelet-based method to study the Gaussianity of the recovered
CMB and compare it with the same analysis for the input map. When the original
CMB map is Gaussian (and assuming that point sources have been removed), we
find that none of the methods introduce non-Gaussianity (NG) in the CMB
reconstruction. On the contrary, if the input CMB map is non-Gaussian, all the
studied methods produce a reconstructed CMB with lower detections of NG than
the original map. This effect is mainly due to the presence of instrumental
noise. In this case, MEM tends to produce slightly higher non-Gaussian
detections in the reconstructed map than WF whereas the detections are lower
for the LCFC. We have also studied the effect of point sources in the MEM
reconstruction. If no attempt to remove point sources is performed, they
clearly contaminate the CMB reconstruction, introducing spurious NG. When the
brightest point sources are removed from the data using the Mexican Hat
Wavelet, the Gaussian character of the CMB is preserved. However, when
analysing larger regions of the sky, the variance of our estimators will be
appreciably reduced and, in this case, we expect the point source residuals to
introduce spurious NG in the CMB. Thus, a careful subtraction (or masking) of
point source emission is crucial when studying the Gaussianity of the CMB.
