  Achieving maximum scientific results from the overwhelming volume of
astronomical data to be acquired over the next few decades will demand novel,
fully automatic methods of data analysis. Artificial intelligence approaches
hold great promise in contributing to this goal. Here we apply neural network
learning technology to the specific domain of eclipsing binary (EB) stars, of
which only some hundreds have been rigorously analyzed, but whose numbers will
reach millions in a decade. Well-analyzed EBs are a prime source of
astrophysical information whose growth rate is at present limited by the need
for human interaction with each EB data-set, principally in determining a
starting solution for subsequent rigorous analysis. We describe the artificial
neural network (ANN) approach which is able to surmount this human bottleneck
and permit EB-based astrophysical information to keep pace with future data
rates. The ANN, following training on a sample of 33,235 model light curves,
outputs a set of approximate model parameters (T2/T1, (R1+R2)/a, e sin(omega),
e cos(omega), and sin i) for each input light curve data-set. The whole sample
is processed in just a few seconds on a single 2GHz CPU. The obtained
parameters can then be readily passed to sophisticated modeling engines. We
also describe a novel method polyfit for pre-processing observational light
curves before inputting their data to the ANN and present the results and
analysis of testing the approach on synthetic data and on real data including
fifty binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB)
database and 2580 light curves from OGLE survey data. [abridged]
