Current efforts to perform automatic image measurement and classificat
ion are reviewed. As an example, we discuss the acquisition, calibrati
on, and star-galaxy classification of O- and E-band imagery from POSS
I obtained with the Minnesota Automated Plate Scanner (APS). For galax
ies with isophotal diameters (mu(B) = 24.5 mss) larger than 25'', it i
s shown that a variety of two-dimensional photometric a parameter spac
es provide a crude segregation of Hubble types. Initial results are pr
esented on the training and testing of two artificial neural networks
developed to map input image parameter vectors to an eight-step morpho
logical-type scale.