Pulsed Eddy Current (PEC) is a new emerging Non Destructive Evaluation technique for sub-surface defect detection. It provides new challenges to signal analysis and interpretation approach applied to the inspection evaluation. For instance, PEC could suffer from noise and be not sufficient to extract more information about the defects. This paper aims to approach the challenge of flaw identification in PECs. Due to non-Gaussianity of PEC measurements, we applied Independent Component Analysis (ICA) in extracting information from PEC responses. We considered three different approaches implementing ICA, in order to project the response signals of various defects into the Independent Components (ICs) feature space. Then, useful ICs of each algorithm were used as features for machine learning algorithms, in order to solve the inverse problem of pattern classification. Since the nongaussianity of the OEC measurements, we retained ICs with highest kurtosis. The considered different kinds of defects were: metal loss, sub-surface cracks, surface defects and slants. We compared the performances of our implemented algorithms with results available in scientific literature. We obtained improvements in reliability of the pattern classification algorithm, as well as in reducing the computational load, obtaining a classification error of 8.54% over 3063 testing patterns.