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Aging has extensive impacts on brain cognition. In this work we proposed a method using Hjorth parameters to classify the elderly’s electroencephalography (EEG) signals from that of middle age group by applying K-nearest neighbor (KNN) and Random forest (RF) classifiers. We acquired EEG of 20 healthy middle age subjects and 20 healthy elderly subjects in resting state eyes-open for 5 minutes and eyes-closed for 5 minutes using an 8-electrodes device. Euclidean and Manhattan distance measures were tested using KNN. The classifier performance was evaluated by using accuracy, sensitivity, specificity, and kappa statistic. The best accuracy achieved was 91.25 %, and kappa statistic of 0.825, in eyes-closed state. In eyes-open state 90% accuracy was achieved with kappa statistic of 0.80. RF achieved 83.75% accuracy with kappa statistic of 0.675 in eyes-closed state and 78.75% accuracy with Kappa statistic of 0.575 in eyes-open state. The KNN performed better using Manhattan distance function in both eyes-open and eyes-closed states. Results showed the potential of Hjorth parameters as the suitable EEG features in the classification of EEG aging signals.