  One of the main obstacles in extracting the Cosmic Microwave Background (CMB)
signal from observations in the mm-submm range is the foreground contamination
by emission from galactic components: mainly synchrotron, free-free and thermal
dust emission. Due to the statistical nature of the intrinsic CMB signal it is
essential to minimize the systematic errors in the CMB temperature
determinations. Following the available knowledge of the spectral behavior of
the galactic foregrounds simple, power law-like spectra have been assumed. The
feasibility of using a simple neural network for extracting the CMB temperature
signal from the combined CMB and foreground signals has been investigated. As a
specific example, we have analysed simulated data, like that expected from the
ESA Planck Surveyor mission. A simple multilayer perceptron neural network with
2 hidden layers can provide temperature estimates, over more than 80 percent of
the sky, that are to a high degree uncorrelated with the foreground signals. A
single network will be able to cover the dynamic range of the Planck noise
level over the entire sky.
