A sample of 900 colour images (320x 240 pixels) of wood boards was drawn from the University of Chile. Each image was manually labelled according to its largest defect, into one of the following 10 defect categories (following the illustration from left to right, top to bottom): birdseye & freckle, bark & pitch pockets, wane, split, stain, blue stain, pith, dead knot, live knot and hole. The data set, was partitioned into two sets: 600 images for the training set and 300 images for the test set. The performance of the FMMIS algorithm was measured on the test set using the following criteria: number of true positives TP (the number of defects contained by hyperboxes and correctly detected), number of false positives FP (the number of grain lines contained by hyperboxes, i.e., detected as defects), and the average processing time. The area recognition rate (ARR) criterion allows to compare the area of the hyperbox built automatically by FMMIS with the area of the manually placed minimum bounding rectangle.