Abstract: Automatic segmentation of satellite image is the base of the automatic identification of cloud systems. This paper presents a combined method of hierarchical threshold segmentation and neural network to segment the image into separate synoptic systems for identification purpose. The processes include selecting all potential TBB thresholds of cloud segments and plotting temperature contours on satellite images for each threshold, then selecting cloud regions which is identified by a certain temperature contour and best representation of synoptic systems, and finally forming a segmented image by combining these regions. How to select these regions by computer is an experiential and uncertain problem. In this paper it is solved by the up-bottom and bottom-up heuristic and neural network method. A cloud pattern database is established. It includes 177 GMS satellite infrared images with 16 kinds of typical synoptic systems in the summer of 1992-1994, 1997-1998. There are 484 training samples from 32 satellite images and 2280 testing samples from 145 satellite images. The neural network accuracy rate for these training samples is 98.8% and 86.4% for the testing samples. The experiment accuracy rate for the application test is above 90% using testing images of 18-21 July 1997 and 15-17 June 1998.The input of the satellite image automatic segmentation system is GMS images, and the outputs include the boundary chain code, start location, perimeter and a rea of each region. These outputs are useful to further classification of cloud patterns.