Arylpiperazine compounds are promising 5-HT(1A) receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT(1A) receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and by the high correlation coefficients (q(2)=0.76, r(2)=0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT(1A) receptor ligands that are able to improve antidepressant treatment.