We present a new system for the recognition of cursive handwriting that is based on a perceptive model and neural networks. At the high level, our system takes into account several psychological effects such as the word superiority effect. At the low level, it utilizes a global feature extraction method which models how some features might be preattentively detected by the human visual system. It presents a very good tolerance to noise and stroke disconnections and captures most of the information contained in the singular part of the cursive word. At the pre-recognition stage, external letters are better recognized than middle letters. Thus, because it uses a recognition process that is based on an interactive activation mechanism, recognition is performed from the outside to the inside of the word. We have obtained encouraging results.