  Artificial neural networks (ANN) have different applications in Astronomy,
including data reduction and data mining. In this work we propose the use ANNs
in the identification of stellar model solutions. We illustrate this method, by
applying an ANN to the 0.8M$_\odot$ star CG Cyg B. Our ANN was trained using
60,000 different 0.8M$_\odot$ stellar models. With this approach we identify
the models which reproduce CG Cyg B's position in the HR diagram. We observe a
correlation between the model's initial metal and helium abundance which, in
most cases, does not agree with a helium to metal enrichment ratio
$\Delta$Y/$\Delta$Z=2. Moreover, we identify a correlation between the model's
initial helium/metal abundance and both its age and mixing-length parameter.
Additionally, every model found has a mixing-length parameter below 1.3. This
means that CG Cyg B's mixing-length parameter is clearly smaller than the solar
one. From this study we conclude that ANNs are well suited to deal with the
degeneracy of model solutions of solar type stars.
