An Automated System to Classify Stellar Spectra I
  Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around Hbeta, and applies to stars of spectral types A to K with normal (scaled solar) chemical composition. The new tool exploits synthetic spectra based on plane-parallel flux-constant model atmospheres. The input data are high signal-to-noise spectra with a resolution greater than about 0.1 nm.   The output parameters are forced to agree with an external scale of effective temperatures based on the Infrared Flux Method. The system is fast -- a spectrum is classified in a few seconds-- and well-suited for implementation on a web server. We estimate upper limits to the 1-sigma random error in the retrieved effective temperatures, surface gravities, and metallicities as 100 K, 0.3 dex, and 0.1 dex, respectively. 
C. Allende Prieto (McDonald Observatory and Department of Astronomy,   University of Texas at Austin)