Recently years have witnessed an increasing interest in the neural network-based video coding, including end2end and hybrid schemes. To foster the research in this emerging field and provide a benchmark, we propose the Grand Challenge on Neural Network-based Video Coding (GC-NNVC) in the ISCAS 2022. In this paper, we review the grand challenge results and share interesting observations resulted from the challenge. This challenge includes two tracks, the hybrid-based and end2endbased. Different neural network-based coding schemes are evaluated according to their coding efficiency and innovations in methodologies in each track. To facilitate a solid comparison with conventional video coding techniques, the decoded sequences are evaluated in YUV 4:2:0 color format and PSNR is adopted as the distortion metric. Compared with the reference software of HEVC, submissions in hybrid track typically exhibit promising BD-rate reduction while submissions from the end2end track perform worse.