Time-to-market and high product quality standards are pushing the use of automatic visual inspection systems for defect detection in a wide broad of applications. The defect detection of particle boards requires the identification of all the printed and natural wood defects that can occur. The availability of information about the particle board to inspect (e.g. the pattern used to print the surface of the board) could increase heavily the defect detection capability of a quality assessment system. Nevertheless, most of the times the pattern is not available during the defect detection phase (i.e. when the pattern changes quickly or when printing and defect detection are not performed by the same company). We propose a novel approach for pattern extraction based on genetic techniques to identify the printing pattern that can be used in defect classification systems. Experimental results show the valuable pattern extraction capabilities of the proposed approach.