An approach of damage detection based on ESPI and SVM is proposed. ESPI (Electronic Speckle Pattern Interferometry) is a non-contacting measuring method, which can measure the small static and dynamitic surface deformations and reveal the flaws by looking for flaw-induced deformation anomalies. Support Vector Machine (SVM) is a machine learning algorithm based on statistical learning theory, and it has recently been established as a powerful tool for classification and regression problems. To develop the precision of processing the pattern fringe data, the SVM is introduced to process the patterns corrupted by the laser speckle effect. The SVM is trained with fringe patterns generated from a finite element model and a simple model of the laser speckle effect. The output pattern is obtained to flag whether the damage exists or not. The trained SVM is tested for robustness with model generated test patterns of a flat plate. The results show that this approach is a promising and effective for damage detection.