  Innovation in the ground and space-based instruments has taken us into a new
age of spectroscopy, in which a large amount of stellar content is becoming
available. So, automatic classification of stellar spectra became subjective in
recent years due to the availability of large observed spectral database as
well as the theoretical spectra. In the present paper, we develop an Artificial
Neural Network (ANN) algorithm for automated classification of stellar spectra.
The algorithm has been applied to extract the fundamental parameters of some
hot helium rich white dwarf stars observed by the Sloan Digital Sky Survey
(SDSS) and B-type spectra observed at Onderjove observatory. We compared the
present fundamental parameters and those from a minimum distance method to
clarify the accuracy of the present algorithm where we found that, the
predicted atmospheric parameters for the two samples are in good agreement for
about 50% of the samples. A possible explanation for the discrepancies found
for the rest of the samples is discussed.
