The aim of this study was to compare the accuracy of both the maximum likelihood classifier (ML) algorithm and another one based on an artificial neural networks classifier (ANN) algorithm for knotty core identification in CT images of pruned radiata pine (Pinus radiata D. Don) logs. For this purpose, thirty pruned radiata pine logs were chosen and then scanned in an X-ray multi-slice medical scanner (Computed Tomography (CT)). From the total CT images obtained, a sample of 270 CT images was selected for this study. This CT images were classified using both methods and the thematic map obtained afterwards, were filtered by a 7 x 7 median filter. Quantitative assessment results showed that knotty core can be identified with 98.5 % and 96.3 % accuracy by using the ML and ANN classifiers respectively. Although both algorithms showed a high capacity level to detect knotty core statistical analysis showed significant differences among those accuracy values; this is an indication that the maximum likelihood classifier algorithm shows a better performance compared to the algorithms based on artificial neural networks for knotty core identification in CT images of radiata pine logs.