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This study aims to identify the defective core on computed tomography images (CT) of pruned radiata pine logs, using an algorithm of supervised classifi cation. The classifi cation process was required to identify and separate the defective core from the free defect part and knots. Ten pruned radiata pine logs were scanned into a medical X-ray multi-slice Philips scanner and the resulting CT images at 5 mm. were obtained. A total amount of 270 CT images were classifi ed under with the maximum likelihood classifi er and the resulting thematic maps were fi ltered with a median fi lter of 7 x 7. Then, 90 thematic maps were selected and used to assess the accuracy of the classifi cation process. To accomplish this, the Confusion Matrix and Kappa statistic were obtained using a sample consisting of 70 randomly selected pixels of each thematic map. An accuracy value of 98.5% was obtained for the

Identificación del Cilindro Nudoso en imágenes TC de trozas podadas de Pinus Radiata utilizando el Clasificador de Máxima Verosimilitud