Abstract: Brain Computer Interface (BCI) is becoming more common now a days as a platform used to support communication between the human brain and external hardware, such as a computer or other electronic peripherals. The communication between these two realms are based on reading the EEG signals produced by the brain. Electroencephalography or EEG is a neuroimaging technique through which the brain signals are measured by using an electrode cap. Every action, movement and thought by an individual in known to produce different patterns of EEG signals, which are generated due to electromagnetic activity inside the brain. In this study, a method used to analyze and classify different EEG patterns based on different motor movements performed by an individual. Useful features are extracted from the pre-processed EEG data by using the Power Spectral Density (PSD) function. These features are then fed as inputs to the neural network classifier for classification process. From the conducted experiments, a high accuracy value was obtained by the classifier in correctly distinguishing the different motor movements by the subject.