This paper presents experimental adaptive identification and control of smart structures using neural networks based on system classification technique. An inverted L-structure with surface-bonded piezoceramic sensors/actuators is used for analysis. The state space, as well as matrix fraction description presentation, from control input voltages to output sensor voltage, is established in multivariable form. It is observed that the computational time required for online parameter identification and controller design is generally quite high. For the system, whose parameters change abruptly with large amplitudes, classical adaptive control techniques give poor transient behavior and sometimes instability. Also, for obtaining the ideal closed-loop performance, linear quadratic regulator cannot be re-designed in real-time for changed parameters of the smart structures, even if these parameters are identified in real time. Closed-loop identification of system parameters and control gains using system classification-based neural networks is proposed and implemented. A preliminary experimental study is also done to see the effectiveness of the proposed technique over classical control methods. © 2007 Elsevier Ltd. All rights reserved.