This dissertation investigates the use of Artificial Neural Networks (ANNs) for character recognition, especially handwritten digits. Real samples of isolated and postal code digits were used from different writers. The dissertation covers four main part: the study of methodologies, semantics and structure on character recognition and its representation; the development of the digits preprocessing phases; the design of ANNs to handwritten digits recognition; and the case studies. The first part of this dissertation studies methodologies, semantics and structures used on character recognition. The result of this study is an overview of the major aplication in OCR (Optical Character Recognition). Different kinds of semantics and their structures were classified according to each specific application. Several conventional models and intelligent systems, used in the classification stage of OCR systems, had also been discussed. The development of the digits preprocessing involved the investigation of different methodologies related to each preprocessing phase. The most used algorithm for each preprocessing phase were considered: thresholding, smoothing, segmentation and normalization. According to this study, specific algorithms were selected and developed. In the design of ANNs for handwritten digits recognition, different methodologies had been investigated, including the architetures and the learning algorithms most used. This overview confirmed the predominance of BackPropagation as the training algorithm for the Neural Network in this application. The architetures proposed in this work had been selected according to two types of applications of character recognition: isolated handwritten digits recognition and postal address code recognition. The case studies consisted of the designing of an ANN to postal address code recognition. The case studies involved testing the system performance for two kinds of ANNs: fully connected networks and partially connected networks. In both cases, samples of 73 writers were used. The results were compared to each other, confirming the superiority of partially connected ANN in handling noisy digits. The ANN perfomance was also compared with the perfomance of other conventional techniques, achieving better results in many cases.