The complexity of epilepsy created a fertile ground for further research in automated methods, attempting to help the epileptologists’ task. Over the past years, great breakthroughs have emerged in computer-aided analysis and the advent of Brain Computer Interface (BCI) systems has greatly facilitated the automated seizure analysis. In this study, an evaluation of the window size in automated seizure detection is proposed. The EEG signals from the University of Bonn was employed and segmented into 24 epochs of different window lengths with 50% overlap each time. Statistical and spectral features were extracted in the OpenViBE scenario that were used to train four different classifiers. Results in terms of accuracy were above 80% for the Decision Trees classifier. Also, results indicated that different window sizes provide small variations in classification accuracy.