Examination of various feature selection algorithms has led to an impr
ovement in the performance of a probabilistic neural network (PNN) clo
ud classifier. These algorithms reduce the number of network inputs by
eliminating redundant and/or irrelevant features (spectral, textural,
and physical measurements). One such algorithm, selecting 11 of the 2
04 total features, provides a 7% increase in PNN overall accuracy comp
ared to an earlier version using 15 features. This algorithm employs t
he same search procedure as before, but a different evaluation functio
n than used previously, which provides a similar bias to that of the P
NN classifier. Noticeable accuracy improvements were also evident in i
ndividual cloud-type classes.