A brain-actuated wheelchair can be used to aid the movement of differentially enabled communities who face much difficulties while commuting from one place to another. In this research work, the active brain signals emanated from subjects while performing four different kinesthetic motor imagery tasks are recorded using electroencephalography (EEG). Three different feature sets, namely, fractal dimension (FD), Mel-frequency cepstral coefficients (MFCCs) and combined features of FD with MFCCs are extracted from the recorded EEG signals. The extracted features are then associated to classify the type of motor imagery tasks and three multi-layer Perceptrons trained with Levenberg-Marquardt method are developed. The performance of the three Perceptron models are evaluated in term of classification rate and compared. From the results, it is observed that the Perceptron model trained with combined features of FD with MFCCs has yielded a higher classification accuracy for all 5 subjects in the range of 93.75-97.96%. The obtained result clearly indicates that the combined features of FD with MFCCs has potential to classify the four different motor imagery tasks.