Abstract: The lithology is rather complex and difficult to identify in igneous reservoirs. With little formation information, traditional cross-plot and supervised neural networks (such as BP network) are restricted in identifying lithology. Therefore, in the south part of SongLiao basin, based on the principles and structure of SOM neural network, the data set of igneous samples were established by actual logging data. The cluster results were obtained by training the samples with SOM network. the influence of standard means, structure parameters and log of SOM network on cluster results, which shows that good results can be achieved for lithology recognition on logging data of igneous reservoir by using normal standard method, selecting proper structure parameters and log, and taking the cluster results as the basis of classification.