  The growth of sky surveys and the large amount of stellar spectra in the
current databases, has generated the necessity of developing new methods to
estimate atmospheric parameters, a fundamental task on stellar research. In
this work we present a comparison of different machine learning algorithms,
using for the classification of stellar synthetic spectra and the estimation of
fundamental stellar parameters included T_eff(K), log(L/Lo), log g, M/Mo, and
Vrot. For both tasks, we established a group of supervised learning models, and
propose a database of measures with the same structure to train the algorithms.
This data includes equivalent-width types measurements over noisy synthetic
spectra in order to replicate the natural noise on a real observed spectrum.
Different levels of signal to noise ratio are considered for this analysis.
