Outliers are the deviation objects of data points. The paper presents an outlier recognition algorithm based on grid adjacency relation (GAO), according to the high or low density of the outlier unit comparing to its neighborhood. The outlier and the outlier unit are determined by the degree of deviation, which is measured by the relative density and distance of the center of mass between units. The experimental results show that the algorithm can recognize the outlier of multidensity, highdimensional and large data sets effectively. The algorithm’s efficiency is better than that of the Cellbased algorithms.