  One of the main obstacles for extracting the Cosmic Microwave Background
(CMB) from mm/submm observations is the pollution from the main Galactic
components: synchrotron, free-free and thermal dust emission. The feasibility
of using simple neural networks to extract CMB has been demonstrated on both
temperature and polarization data obtained by the WMAP satellite. The main goal
of this paper is to demonstrate the feasibility of neural networks for
extracting the CMB signal from the Planck polarization data with high
precision. Both auto-correlation and cross-correlation power spectra within a
mask covering about 63 percent of the sky have been used together with a 'high
pass filter' in order to minimize the influence of the remaining systematic
errors in the Planck Q and U maps. Using the Planck 2015 released polarization
maps, a BB power spectrum have been extracted by \textit{Multilayer Perceptron}
neural networks. This spectrum contains a bright feature with signal to noise
ratios $\simeq$ 4.5 within 200 $\leq$ l $\leq$ 250. The spectrum is
significantly brighter than the BICEP2 2015 spectrum, with a spectral behaviour
quite different from the 'canonical' models (weak lensing plus B-modes spectra
with different tensor to scalar ratios). The feasibility of the neural network
to remove the residual systematics from the available Planck polarization data
to a high level has been demonstrated.
