Precipitation is a basic output flux of atmospheric process and a driving force of hydrological process. Accurate observation of precipitation with highly spatial and temporal variability has long been a challenging scientific goal in the field of hydrometeorology. Multi-sensor Precipitation Estimation (MPE) has been the mainstream trend for retrieving precipitation. And it has been a unique way of providing global high accuracy and High Resolution Precipitation Products (HRPPs). This paper describes the definition and classification of MPE, and briefly summarizes the development and status of its history. The development of MPE can be divided into two parts based on the year 1997. The commonly used MPE algorithms to produce global HRPPs include TRMM Multi-satellite Precipitation Analysis (TMPA) algorithm, climate prediction center morphing (CMORPH) algorithm, Global Satellite Mapping of Precipitation (GSMaP) algorithm, Naval Research Laboratory Blended (NRLB) algorithm and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). Then, the existing problems are put forward through comparing assets and liabilities and accuracy of the five algorithms. The MPE can be roughly categorized into two methodologies: adjustment-based techniques (TMPA and NRLB) and the motion-based techniques (CMORPH and GSMaP). The adjustmentbased techniques have the longest data record, but inherently rely upon an assumption of indirect relationship between IR temperatures and rainfall rates. The motion-based techniques can provide rain rate at desired intervals. One disadvantage of this approach, however, is that the cloud tops detected by the IR imagery can move at speeds different than the precipitation features below them, and precipitation may not be properly accounted for. At present, no one algorithm performs best in any regime. HRPPs algorithms generally tend to perform best in the convective situations during summer but dropped off considerably when moving into winter and higher latitudes with varied orography. PERSIANN overestimates heavy rainfall (200%) while underestimates rainfall (56%) in the mountains. The other four HRPPs underestimate rainfall ranging from 3 to 7 mm/d(10%~67%). For future development, advanced and/or new MPE algorithms will be proposed with analyzing existing algorithms. Furthermore, the Global Precipitation Measurement (GPM) mission will be improved and extend the TRMM measurement to high latitudes, with a more frequent sampling and higher sensitivity to light and heavy rainfalls. In addition, more focus will be taken on quantitatively evaluating accuracy of HRPPs.