In this paper a combination of brain emotional learning based intelligent controllers (BELBICs) is employed to control an unidentified practical overhead crane. The proposed controller is a model free controller and has the capability to deal with multi-objective control problems. These properties make BELBIC a powerful controller for unknown complex systems when identification in not cost effective or cannot be performed. The fast emotional learning capability has resulted in good performance even in short procedure (training) time, which is very important in real time model free control. The proposed controller is implemented on a laboratorial overhead crane1 in a real time model free control task. Two different loads are added to the crane set to simulate uncertainties of an actual crane. To consider main and extra objectives a nonlinear combination of them is used to generate emotional stress signal. Experimental results show that the proposed control system has so rapid and powerful learning capability that can eliminate any need for prior system identification. The results are compared with original sample controller and HFLCANFIS. In comparison with ANFIS compensator it has faster compensation of tracking and regulating of the load swings and in rejecting of disturbances. Also, in presence of disturbances, BELBIC performance does not decrease significantly and is slightly better than the others.