For the segmentation of X-ray angiograms (XRA), the essential feature and the prior knowledge of angiographic image were analyzed, and a multi-feature based fuzzy recognition (MFFR) algorithm was proposed to infer the local vessel structure in this paper. Guided by the prior knowledge of artery vessel, a probability tracking operator (PTO) can rapidly track along the artery tree, and walk across the weak region or gaps because of disturbance or preprocessing to angiographic image. Another, the accurate measurement of the vascular axis-lines and diameters can be synchronously implemented in the tracking process. To correctly evaluate the proposed method, a simulated image of CAT and some clinical XRA images were used in the experimentations. The algorithms performed better than the conven- tional one: given one start-point, on average 92.7% of the visible segments or branches was automatically delineated; the correctness ratio of vessel structure inference reached to 90.0% on the average. For the segmentation of X-ray angiograms (XRA), the essential feature and the prior knowledge of angiographic images were analyzed, and a multi-feature based fuzzy recognition (MFFR) algorithm was proposed to infer the local vessel structure in this paper. by the prior knowledge of artery vessel, a probability tracking operator (PTO) can rapid track along the artery tree, and walk across the weak region or gaps because of disturbance or preprocessing to angiographic image. Another, the accurate measurement of the vascular axis- lines and diameters can be synchronized implemented in the tracking process. To correctly evaluate the proposed method, a simulated image of CAT and some clinical XRA images were used in the experimentations. The algorithms performed better than the conven- tional one: given one start- point, on average 92.7% of the visible segments or branches was automatically delineated; the correctness ratio of vessel structure inference reached to 90.0% on the averag e.