The human brain, which receives input from the sensory organs and sends output to the muscles, is the command center of the nervous system. There are various kinds of brain monitoring techniques including computed tomography, magnetic resonance imaging (MRI), positron emission tomography, functional MRI, electroencephalography (EEG) and magnetoencephalography. Among those of techniques EEG is the most widely used one due to its portability, low set-up cost and noninvasiveness. In this work, the EEG signals, which were recorded during smelling of valerian and rosewater odors, were analyzed and classified based on features which were extracted using Fast Fourier Transform. EEG signals were taken from 5 healthy subjects in the conditions of eyes open and eyes closed at Swiss Federal Institute of Technology. We achieved a mean classification accuracy rate of 90.73 % for the subjects at eyes closed condition and a mean classification accuracy rate of 92.21 % for the subjects at eyes open condition using k-nearest neighbor algorithm. The reached results prove that the proposed method have great potential for classifying the EEG signals recorded during smelling of valerian and rosewater odors and instead of using subject-specific model it can be generalized and applied to all subjects.