A hopfield type neural network was applied to optimize binary correlation synthetic discriminant functions (SDFs). Rotation invariance is achieved while the target object rotates in a certain angle range and a ratio for judgement which is defined as the ratio of the peak value to the average absolute value of a specific point set is given. The optimized binary SDFs (BSDFs) provide the control of the sidelobe levels and the expected shape of the output correlation functions as well as its peak intensity. The simulation result shows that when the target object is presented to the optimized filter, not only the correlation peak is as high as expected and higher than that of the non-target objects, but also the order of the magnitude of the ratio for judgement is at least 1 greater than that of the non-target objects. The recognition ability of the filter is very strong.