  We present an algorithm based on artificial neural networks (ANNs), that
estimates the mass ratio in a binary black hole collision out of given
Gravitational Wave (GW) strains. In this analysis, the ANN is trained with a
sample of GW signals generated with numerical simulations. The effectiveness of
the algorithm is evaluated with GWs generated also with simulations for given
mass ratios unknown to the ANN. We measure the accuracy of the algorithm in the
interpolation and extrapolation regimes. We present the results for noise free
signals and signals contaminated with Gaussian noise, in order to foresee the
dependence of the method accuracy in terms of the signal to noise ratio.
