  Identification of metal-poor stars among field stars is extremely useful for
studying the structure and evolution of the Galaxy and of external galaxies. We
search for metal-poor stars using the artificial neural network (ANN) and
extend its usage to determine absolute magnitudes. We have constructed a
library of 167 medium-resolution stellar spectra (R ~ 1200) covering the
stellar temperature range of 4200 to 8000 K, log g range of 0.5 to 5.0, and
[Fe/H] range of -3.0 to +0.3 dex. This empirical spectral library was used to
train ANNs, yielding an accuracy of 0.3 dex in [Fe/H], 200 K in temperature,
and 0.3 dex in log g. We found that the independent calibrations of near-solar
metallicity stars and metal-poor stars decreases the errors in T_eff and log g
by nearly a factor of two. We calculated T_eff, log g, and [Fe/H] on a
consistent scale for a large number of field stars and candidate metal-poor
stars. We extended the application of this method to the calibration of
absolute magnitudes using nearby stars with well-estimated parallaxes. A better
calibration accuracy for M_V could be obtained by training separate ANNs for
cool, warm, and metal-poor stars. The current accuracy of M_V calibration is
(+-)0.3 mag. A list of newly identified metal-poor stars is presented. The M_V
calibration procedure developed here is reddening-independent and hence may
serve as a powerful tool in studying galactic structure.
