Purpose of the paper is to present a new application of a Non Destructive Test based on vibrations measurements, developed by the authors and already tested for analysing damage of many structural elements. The proposed new method is based on the acquisition and comparison of Frequency Response Functions (FRFs) of the monitored structure before and after an occurred damage. Structural damage modify the dynamical behaviour of the structure such as mass, stiffened and damping, and consequently its FRFs, making possible to identify and quantify a structural damage. The activities, presented in the paper, mostly focused on a new FRFs processing technique based on a dedicated neural network algorithm aimed at obtaining a “recognition-based learning”; this kind of learning methodology permits to train the neural network in order to let it recognise only “positive” examples discarding, as a consequence, the “negative” ones. Within the structural NDT a “positive” example means “healthy” state of the analysed structural component and, obviously, a “negative” one means a “damaged” or perturbed state. The developed NDT has been tested for identifying and analysing damage on an aeronautical composite panel to validate the method and calibrate the neural network algorithm. These tests have permitted to understand the influence of environmental parameters on the neural network training capability. Thanks to these new techniques it is possible to carry out a smart Health Monitoring system which is going to lead to the reduction of time and maintenance cost and to the increase of the aeronautical structure safety and reliability.