Artificial neural networks(ANNs)whose elements are inspired by biological nervous systems are composed of simple elements operating in parallel.Commonly neural networks are adjusted,or trained,so that a particular input leads to a specific target output.Neural networks have been trained to perform complex functions in various fields of application including predic-tion,pattern recognition,system identification,classification and optimization.Conventional statistical models fail to deal with non-linear relations among the physical factors.However,as an alternative approach,ANNs can map complex temporal and spatial pat-terns by using non-linear transfer functions.In this paper,regionalization of ecological assets is conducted by unsupervised artificial neural network,namely Self-Organizing Feature Mapping(SOFM).The field data employed as input for training represent spatial ecological features such as longitude,latitude,annual mean temperature,annual mean precipitation,aridity,biological tempera ture,assets demand index,assets scarcity,NPP of unit area and ecological value of unit area collected at84sites on Qinghai-Tibet Plateau.After the iterative learning phase in the SOFM analysis,each of the84sites is associated with an output unit.Each output unit contains some of the sites and there is obvious discrete grouping of cases.The SOFM,therefore,appears to have organized the sites such that the various output units are associated with different eco-logical assets classes.In order to assess the performance of SOFM,the comparison with cluster analysis is carried out.The result indicates that the overall performance of the neural network algorithm was better than that of cluster analysis for ecological regionalization.Finally,using SCS paradigm,conver-sion from SOFM classification to ecological assets regionalization is conducted.ternsbyusingnon linearconductedbyunsupervisedSOFM.Thefielddata