To achieve identification of the flight mechanical parameters of aircraft experimental techniques like free flight or wind tunnel tests are still essential, even if the numerical methods are getting better. But the mentioned techniques have both their disadvantages: On the one hand because the mounting of the model during wind tunnel measurements causes interference to the flow. On the other hand coupling effects caused by the movement of the aircraft in the flow can only determined with high costs – in some cases it is even not possible. The disadvantages of the free flight techniques consist of no reproducible conditions (atmospheric disturbances), high costs and the risk of manned test flights. The goal of this work which was conducted at the Chair of Flight Dynamics at the RWTH Aachen University was the creation of a reproducible free flight environment for the cost effective Identification of main flight mechanical parameters even in an early design stage. Therefore the advantages of the free flight techniques will be combined with the advantages of the Wind Tunnel techniques by bringing the free flight into the wind tunnel under laboratory conditions. The position and attitude of the aircraft is affiliated by a 3D Camera System with high frequency and accuracy. Hence, the aircraft must only be equipped with sensor for the control surface positions and the revolution of the engine. The aircraft and the whole process are controlled by a real time system, which is implemented in Matlab/Simulink before. An adaptive identification algorithm, based on a regression in the frequency domain, generates the required excitation manoeuvre and analyses them in real time. Because the algorithm is adaptable, it needs only little a priori knowledge of the aircraft characteristics. The manoeuvre will be adapted until they fit most exactly to the eigenfrequency of the aircraft. The control algorithm has the function to reposition the aircraft after an identification manoeuvre and to trim. Additional, the algorithm takes control of the aircraft if it reaches the border of the free stream and tries to reposition it in the center of the wind tunnel. Because of the high non linearity and the agility of the aircraft the dynamic inversion is used. This is enhanced by a pseudo control hedging to avoid non linear rate- and deflection limits and to avoid the inversion of the dynamic of the actuator. As this combination is not very robust against uncertainties in the parameters, as they appear especially in this case, additionally adaptive terms in form of neural networks where included. After the free flight experiment an identification based on offline algorithms is conducted. Because of the higher resources even nonlinear models and better filter algorithms can be used. Because of the sensor concept, drift and bias in the signals do not exist. Therefore an equation error method is adequate. The Validation exemplary takes place with an aircraft in the wind tunnel of the Chair of Flight Dynamics.