A novel approach of three-dimensional (3-D) object rotation-toleran t classification and recognition using synthetic discriminant function (SDF) is proposed based on integral imaging (II) system.In this approa ch,elemental image arrays of 3-D rotational object can be obtained by the II system,and 3-D rotation-tolerant classification and reco gni tion is realized by using the high relevancy characteristic in the information of 3-D object collected by the elemental image arrays and SDF.T he intention of SDF is to find a filter function that can obtain the same correlation output if rotational objects are input.But gene ral SDF needs more data to improve the recognition accuracy, which result in greater computational complexity and lower practicability.Meanw hile,high relevancy of perspective elemental image array of II system can overcome the above shortcoming of SDF.In compari son wit h other approaches,by introducing SDF into the field of 3-D object recognition,and combining the merits of II and SDF,the approach in this paper solves the contradiction between recognition accuracy and data amount,provides a novel recognition theory and therefore can greatly reduce the data amount during the recognition process and enhance the recognition efficiency.In addition,this approach can be applied for random rotational angles.Rotation-tolerant classification and recognition has been re alized for several kinds of rotational 3-D objects in this paper and the effectiveness has also been validated by experiment.