In 2010, Microsoft launched a depth camera named Kinect, which can synchronously provide the depth and RGB information of a scene. Using sensing technique, robust depth information can be achieved at real time. One key area of Kinect is object recognition. Most of the traditional object recognition techniques are limited in special situations, such as gesture recognition, face recognition. Recent trend in computer vision is large-scale recognition. We can get the RGB-D datasets from various scenes, categories, instances and viewpoints of real domestic and office environment by Kinect. The RGB-D datasets can meet the need of practical applications. The depth information provides a strong clue for object recognition. Compared with the previous methods, using depth information for object recognition has incomparable advantage, which can greatly improve the accuracy of recognition. The four major components of this paper are as follows: 1) a detailed introduction of the technique to obtain depth information using Kinect; 2) review of the existing 3D Object recognition methods; 3) an analysis and comparison of the existing 3D Datasets for testing; 4) summary of the article, and the future development trend of the 3D object recognition algorithms and the 3D datasets for testing.