The processing of naturally occurring information (e.g. the examination of the visual field by a pattern recogniser) can be significantly simplified if the raw information is initially processed in such a way as to enhance those features that are important with respect to the given task whilst attenuating the spurious or undesired information. A convenient method of specifying what information is to be removed and what preserved in, for example, the class of one-dimensional signals typified by the output from a microphone is to use the Laplace transform to generate a suitable realisable filter whose frequency characteristics can be easily and accurately specified. A severe drawback associated with using these filters however is that, unavoidably with the class of signals mentioned above, the impulse response function is asymmetric. This asymmetry is responsible for the well-known phase shift effect produced by electrical filters. Such filters can be derived and analysed by means of the one-sided Laplace transform, the one-sidedness resulting from the limitations imposed by physical realisability. This note describes a method of using the one-sided Laplace transform to produce N-dimensional, approximately spherically symmetric filters. Although these are derived in numerical form they can be produced just as readily by means of actual hardware provided the appropriate data scanning is carried out. The method is compared with the mesh operator approach and its relative merits are enumerated. The effect of these filters, which operate recursively, is demonstrated by their application to the enhancement of fingerprint images—a notoriously difficult cleaning-up problem. Fingerprints, by virtue of their almost constant width line structure have a very narrow two-dimensional band-width. Thus it is possible, by centring a band pass filter on the dominant frequency for a given fingerprint, to remove a great deal of the spurious data that is inevitably included by the data-capturing process.