Abstract: Artificial neural network pattern recognition technique(ANN),as a simulation and abstraction of human being’s brain thoughts，can be used to recognize and classify objectives by imitating the transmission manner of nerve cel1．The most popular ANN model at present is the error back propagation, which trains the nerve network by back propagation algorithm．A typical back propagation nerve network has three-layer feed forward structure，consisting of input layer， cryptic layer and output layer. In this study, the ANN method is applied to recognize the remaining oil of thin and poor reservoir of Daqing oilfield，associated with the data of sealing coring inspection well and development geological method. The process is first to input the known parmeters related to the formation and distribution of remaining oi1；then select the suitable mathematic algorithm for calculation，and finally to obtain the parameters，including the accurate oil saturation，water-bearing and water flooding degree．However，the key of this technique is to determine the input parameters which are related to formation mechanism and distribution of remaining oil．The authors analysed the development conditions and producing status of the thin and poor reservoir of Xing 2-1-Jian 29 well，which is located at Xingshugang，a typical district of oil field of Daqing．The results show that geological factor and the development factor are both important affecting the distribution of remaining oil．The remaining oil is usually distributed in the districts of sand bodies with discontinuous growth or incomplete injection-production. The main parameter of ANN for recognizing the remaining oil of single well and single stratum is sand body type. The recognition model of water flooding degree and oil saturation is established by the nerve network training．Th model was tested by the data of other sealing coring inspecting wells, and the average error was 8．4 ％ , which indicates that the recognition mode1 is good in use．The authors applied this model to densified wel1 pattern testing district in Sa’ertu oil field of Daqing to analyse and interpret the water-out degree of perforated reservoir，which could predict the water-out distribution of thin and poor reservoirs effectively