Oil spilling is one of the major sources for in marine pollutions,which are widely distributed and can bring cause terrible significant environmental damages. In recent years,due to the increase in offshore human activities and development of petroleum processing industries,oil spill accidents are also increasing,which are mostly caused by well blowouts,explosions of drilling platforms and ship collisions. Therefore,monitoring oil spilling has important significance in both economical and social aspects. As an all-weather high-resolution active microwave imaging sensor,Synthetic Aperture Radar (SAR) can greatly improve the resolution of images and the accuracy of forecasts,and thus takes an important role in oil spill monitoring. This paper aims to realize the semi-automatic identification of various targets on SAR images. We have conducted a convincing contrast of different neural networks,using Matlab as the tool through image preprocessing (image correction and enhancement),feature extraction and neural network recognition. First,oil spilli images are preliminarily manually identified,followed by image preprocessing (such as geometric correction,filtering,etc.) and feature extraction based on gray level co-occurrence matrix. Then,two types of neural networks,namely RBF and BP ,are introduced to classify the oil spill area and other suspected areas. Finally,the processed images are analyzed,indicating the capability in classifying oil,sea water,and land targets. The results reveal that the outputs from the RBF neural network are more accurate compared to those from the BP neural network.