EEG signals are measured on scalp of the human brain and are widely used to address the clinical as well as in modern application like brain computer interfacing (BCI) and gaming. Feature extraction plays a fundamental role for good classification purposes. EEG features commonly extracted are linear as well as nonlinear. Nonlinear approaches are used when the complexity of EEG signals increases. Nonlinear features like correlation dimension (CD), Lyapunov exponents, approximate entropy requires higher computational complexity. On other hand Fractal dimension (FD) requires less computations. Therefore, Fractal dimension are widely used in engineering and biological sciences. In our paper, Fractal dimension has been selected to discriminate the different brain states. EEG data from 08 healthy participants have been acquired during eyes open, eyes close and during IQ task. Fractal dimensions have been computed on the EEG data acquired. Using Fractal dimension, we have successfully discriminated the different brain conditions/states like eyes open, eyes close and IQ task. Results have shown better discrimination between mental task and active brain conditions with 91.66 accuracy using SVM classifier as compared to other classifiers. This approach can be used for fast decision making and pattern matching based on the selected epoch of the EEG signal using nonlinear approach. © 2014 IEEE.