  We investigate the application of neural networks to the automation of MK
spectral classification. The data set for this project consists of a set of
over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates
from the Michigan Spectral Survey. These spectra, along with their
two-dimensional MK classifications listed in the Michigan Henry Draper
Catalogue, were used to develop supervised neural network classifiers. We show
that neural networks can give accurate spectral type classifications (sig_68 =
0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types
present in the data set (B2-M7). We show also that the networks yield correct
luminosity classes for over 95% of both dwarfs and giants with a high degree of
confidence.
  Stellar spectra generally contain a large amount of redundant information. We
investigate the application of Principal Components Analysis (PCA) to the
optimal compression of spectra. We show that PCA can compress the spectra by a
factor of over 30 while retaining essentially all of the useful information in
the data set. Furthermore, it is shown that this compression optimally removes
noise and can be used to identify unusual spectra.
