  Large scale, deep survey missions such as GAIA will collect enormous amounts
of data on a significant fraction of the stellar content of our Galaxy. These
missions will require a careful optimisation of their observational systems in
order to maximise their scientific return, and will require reliable and
automated techniques for parametrizing the very large number of stars detected.
To address these two problems, I investigate the precision to which the three
principal stellar parameters (Teff, logg, [M/H]) can be determined as a
function of spectral resolution and signal-to-noise (SNR) ratio, using a large
grid of synthetic spectra. The parametrization technique is a neural network,
which is shown to provide an accurate three-dimensional physical
parametrization of stellar spectra across a wide range of parameters. It is
found that even at low resolution (50-100 AA FWHM) and SNR (5-10 per resolution
element), Teff and \met can be determined to 1% and 0.2 dex respectively across
a large range of temperatures (4000-30000 K) and metallicities (-3.0 to +1.0
dex), and that logg is measurable to +/- 0.2 dex for stars earlier than solar.
The accuracy of the results is probably limited by the finite parameter
sampling of the data grid. The ability of medium band filter systems (with
10-15 filters) for determining stellar parameters is also investigated.
Although easier to implement in a unpointed survey, it is found that they are
only competitive at higher SNRs (> 50).
