There is a lot of uncertain information in target recognition which can be learned and reasoned by the use of BN.However the target recognition problem is of small sample size and there are often some errors due to lack of observational data during parameter learning.Therefore it is needed to introduce monotonic expertise.Focusing on the above problem the minimum unit algorithm was proposed.By use of the minimum unit the monotonic information was translated into priori information which could be directly used in parameter learning.Then based on the thinking of isotonic regression the parameter learning outcomes were optimized and the errors were eliminated.The relatively accurate network parameters were obtained.On the background of aerial target recognition the advantages of minimum unit algorithm compared with the minimum lower sets algorithm in accuracy and complexity are illustrated.