In this paper a method for on-line signature verification is presented. The proposed approach consists of the following consecutive steps: feature selection and classification. Experiments are carried out on SUsig database [
] of genuine and forgery signatures of 89 users. The results obtained by applying two different types of classifiers (NN and
-nearest neighbours) are compared. For each user, several NN and kNN models are evaluated by 10-fold cross validation and LOOCV respectively. The “optimal” models are found together with their parameters: number of hidden neurons for NN, type of signature forgeries for training, input features and value of
. The influence of the signature forgery type (random and skilled) over the feature selection and verification is investigated as well.