New adaptive correlation filters for reliable recognition of geometrically distorted objects in blurred and noisy scenes are proposed. The filters are based on modified synthetic discriminant functions. The information about objects to be recognized, false objects, disjoint background, additive noise, and expected degradations of targets and input scenes are utilized in an iterative training algorithm. The algorithm is used to design a correlation filter with a specified discrimination capability. Computer simulation results obtained with the proposed adaptive filters in test scenes are discussed and compared with those of various correlation filters in terms of discrimination capability and location errors.