Sampling of the Fourier transforms of fingerprints is studied with neural networks to detect regions useful for their classification. Ring-wedge detector (RWD) is modified and simulated to sample such regions. The output of the detector is propagated through a three-layer feedforward-backpropagation neural network for checking the classification performance. Modified detector's performance is also compared with that of RWD. It has been found that fingerprints scanned at 500 dpi resolution and cropped to a size of 200×200 contain useful information for their classification in a band of width 20 pixels with inner radius approx. 60 pixels.