Hyperspectral image processing techniques are utilized for a variety of applications from geological surveys to detection of camouflaged enemy vehicles. One of the persistent problems is that huge amounts of data must be processed, since a hundred or more frequency bands of spectral information can make up a typical hyperspectral image cube. If real time processing is necessary, as in target tracking or identification, some means of selecting which bands are relevant to the image and which bands can be safely ignored is desirable. We propose a fast, easily trainable neural network filter architecture that can rapidly screen a hyperspectral image cube in near real time. A bank of filters, operating in parallel, is used to screen an image for suspected targets. Performance on simulated and real images is compared to existing recognition techniques and results in considerable reduction in overall image processing time and greater accuracy.