In this study Self-Organizing Map (SOM) neural network has been applied for clustering of high-dimensional geological and geophysical data of Iran resulting numerical tectonic zoning. Visualization of high-dimensional data in two-dimensional topology-preserving feature map (visualization of clusters) and also visualization of component planes (visualization of variables) are the other specific capabilities of SOM used in this study. The component planes constructed here by SOM are successful in determining the effective parameters in tectonic zoning. Although, there are some compatibilities between numerical maps constructed here and the conventional maps but SOM provides more detailed identification and reliable interpretation about different zones. Based on the especial properties of SOM, some similarities and dissimilarities between different zones, despite of their geographical positions, have been revealed that not been noticed in conventional map previously. According to the results of this study SOM is a powerful and suitable method in tectonic zoning especially for regions where their tectonic regionalization is not well known.