Abstract: The pattern recognition of spatial cluster has become a hot issue in the areas of geographical information sciences. Pattern recognition of road networks plays an important role in map generalization, data matching and spatial analysis. Grid pattern is one of the most typical patterns in road networks. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines with roughly right angle. This paper proposes a method for grid pattern recognition based on C4.5 algorithm. Meshes in road networks can be classified as belonging to grid and not belonging to grid according to their context. Firstly, shape measures and relation measures are defined to characterize meshes in road networks. Secondly, two classifiers are trained using C4.5 algorithm based on five measures data and three measures data. A 10-fold cross validation process is applied in order to obtain a sounder result. Finally, the performance of the classifiers is evaluated by means of the Kappa index and the overall correct rate. The Kappa classification accuracy for five dimensions data and three dimensions data is 0.63 and 0.66. The overall correct rate is 81.7% and 82.9% for each. The confidence interval of 90% confidence is [0.785, 0.846] and [0.797, 0.857] respectively. The classifiers are tested by a new data set and the results show that the classifiers are valid in grid pattern recognition. This study tries to use theories and methods of traditional pattern recognition and data mining to solve the spatial issues.