The performance of the WVIMDC which was tested with the test examples in RS1, RS2 and RS3 is given in this slide. The average misclassification of the WVIMDC is 36.88%. A suitable wood veneer inspection neural network was designed with seventeen neurons in the input layer, thirteen neurons in the output layer and one hidden layers with 51 neurons. Only the best of the three of the nine trials conducted for the WVINN with RS1, RS2 and RS3 are given in this table. The average misclassification of the WVINN is 13.48%.