The present study investigates an application of artificial neural networks (ANNs) for use in pharmaceuticalfingerprinting. Several pruning algorithms were applied to decrease the dimension of the input parameterdata set. A localized fingerprint region was identified within the original input parameter space from whicha subset of input parameters was extracted leading to enhanced ANN performance. The present resultsconfirm that ANNs can provide a fast, accurate, and consistent methodology applicable to pharmaceuticalfingerprinting.
