The objective of this study was the investigation of the potential value of morphometry, feature selection and statistical classifiers techniques, such as neural networks, for the classification of benign from malignant gastric nuclei and cases. One hundred and twenty gastric smears, routinely processed and stained by Papanicolaou technique, were analyzed by a customized image analysis system. Data from half of the cases were selected to form the training set, while the remaining data formed the test set. A feature selection technique was applied in order to identify the most important nuclear features, which were used in a second stage by statistical classifiers to classify a nucleus as benign or malignant. Using the classifier results for the nuclear classification, a method to classify each individual patient was developed. The performance of the proposed method was validated through the test set. The technique described in this report produces significant results at the nuclear and patient level and promises to be a powerful assistance tool for everyday cytological laboratory routine.