In metallic structures, the first and second most frequent damages incurred are generally cracks and corrosions. Correct damage classification for these two damages is important since their phases can be developed with dissimilar patterns. In this research, damage classification using the Adaboost machine learning algorithm is investigated. To accomplish this, the physical differences of the two types of damages are defined and the most appropriate excitation signal is also determined. Various time-frequency methods are examined with the sensed damage signals to obtain a suitable signal processing method for damage classification. Among the methods examined, the spectrogram is chosen since it provides reliable results for these types of damages. With these results, the damage classification is performed through the Adaboost machine learning algorithm. The training samples for the algorithm are obtained from a finite element tool and experiments are also performed to get the testing samples. The analysis results show that correct damage classification is feasible using time-frequency representations and the Adaboost machine learning algorithm.© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.