Traditional image segmentation algorithms exhibit weak performance for plant cells which have complex structure. On the other hand, pulse-coupled neural network (PCNN) based on Eckhorn's model of the cat visual cortex should be suitable to the segmentation of plant cell image. But the present theories cannot explain the relationship be. tween the parameters of PCNN mathematical model and the effect of segmentation. Satisfactory results usually require time.consuming selection of experimental parameters. Mean-while, in a proper, selected parametric model, the number of iteration determines the segmented effect evaluated by visual judgment, which decreases the efficiency of image segmentation. To avoid these flaws, this note proposes a new PCNN algorithm for automatically segmenting plant embryonic cell image based on the maximum entropy principle. The algo-rithm produces a desirable result. In addition, a model with proper parameters can automatically determine the number of iteration, avoid visual judgment, enhance the speed of' segmentation and will be utilized subsequently by accurate quantitative analysis of micro.molecules of plant cell. So this algorithm is ainable for theoretical investigation and application of PCNN.