<![CDATA[There are two major class of characteristics for wood species identification. The first class is generalcharacteristics such as colour, odour, wood grain, texture etc, those can be observed directly by commonsense,namely the eyes without using aditional tools ecxept a loupe with at least ten times magnification. Thesecond class is anatomical characteristics which provide wood structure including morphology and type of cellwood components as well as their distribution, which can be observed by using microscope. By the artificialintelligence system, wood species which one commonly used for industries become easier to identify. It takesshorter time compared with the conventional activity. Aim of research was to create the neuro-fuzzy systemmodel, which able to identify wood species for construction and furniture utilizations based on their woodanatomical characteristics, namely vessel element (their distribution, frequency and size) and ray parenchyma(frequency, wide, high). Objects in this research were rubber wood, keruing, kamper, acacia, meranti andjelutong.The Neuro-Fuzzy System developed could identify with 0% errorness if the system using the same data inthe training process or using training sintetic data more than 1000 data.Kata Kunci: anatomic characteristic, neuro-fuzzy, wood class identification.]]>
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