In this paper, new techniques have been introduced for revealing the individual features of a person's handwriting pattern to facilitate text-independent off-line writer identification. These techniques are aimed at designing a dynamic model which can be formalized according to any handwritten text line. Various combinations of the extracted features are applied to three well known classifiers for evaluating the contribution of features to the correct identification rate. The K-NN, GMM, and Normal Density Discriminant Function (NDDF) Bayes classifiers are used in the present identification model. The experimental studies are conducted on the IAM database containing 650 writers. The performance of the extracted features is also analyzed with respect to number of writers in the query. The remarkable identification rates obtained from the three classifiers clearly indicate that the proposed feature extraction techniques can be effectively used in writer identification systems.