An improved Scale Invariable Feature Transformation(SIFT) matching algorithm with global context vector is presented to solve the problems that SIFT descriptors result in a lot mismatches when an image has many similar regions. By detecting feature points in scale space, two kinds of feature vectors, a SIFT descriptor representing local properties and a global context vector, are computed. Then, according to BBF searching strategy, the feature vectors are matched by using Euclidean distance. The experimental results indicate that the improved algorithm can describe feature points in a larger region,and can reduce mismatch probability of experimental images from 19% to 11% because global context vectors based on global shape information are induced to the SIFT vectors based local Information. These results reported above show proposed algorithm improves matching results greatly.