Nowadays, EEG signals are highly used in the field of medical research, such as the treatment of epilepsy, and in the brain-computer interface systems. In this paper, our previously proposed Signal-to-Image Transformation (StIT) method is used for classification of EEG signals. StIT is a kind of finite transformation based on the detection of local maximum and minimum points on an EEG signal. This paper consists of four main stages. In the first stage, arbitrary time domain EEG signals are converted to two-dimensional finite images; in the second stage, principal component analysis is employed for feature extraction; in the third stage, k-nearest neighbour (k-NN), support vector machine, and artificial neural network methods are applied for the classification. The performance of the proposed method is over 94% and the results are better compared to most of the previous ones. The obtained results also show that the EEG signals are quite successfully classified by using the transformation.