There are two major class of characteristics for wood species identification. The firs class is general characteristics such as colour, odour, wood grain, texture etc, those can be observed directly by common sense, namely the eyes whitout using aditional tools ecxept a loupe with at least ten times magnification. The second class is anatomical characteristics which provide wood structure including morphology and type of call wood components as well as their distribution, which,can be observed by using microsope. By the artificial intelligence system, wood species which one commonly used for industries become easier be come easier to identify. It takes shorter time compared with the conventional activity. Aim of research was to create the neuro-fuzzy system model, which able to indentify wood species for constructioon and furniture utilazations based on their wood anatomocal 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 and jelutong. The Neuro-Fuzzy System developed could identify with 0% errornes if the system using the same data in the training process or using training sintetic data more than 1000 data.