Accurate quantitative precipitation estimation (QPE) and forecast are critical for flood and flash flood warnings and for water resource managements. Significant advancements in recent years in computational resources, networking, and remote sensing technologies have provided great opportunities to develop more accurate QPE than what were there before. Given the complex spatial and temporal characteristics of precipitation processes, not one single observing system can provide complete and accurate measurements of surface precipitation for the wide spectrum of hydrological applications. For instance, the rain gauges make direct measurements of the surface precipitation, but the gauge stations are often sparsely distributed and the measurements are subject to errors due to temporary blockage of the collecting orifice by frozen hydrometeors, wind blowing-off effects on the tipping buckets, telemetry errors, etc. Weather radars make semi-direct measurements of the precipitation but there are uncertainties associated with reflectivity (Z) - rain rate (R) conversion in addition to errors associated with beam blockage and non-uniform vertical profile of reflectivity. Satellite data have no obstructions and provide best coverage of precipitation systems among all observational networks, yet they only observe cloud tops and provide indirect measurements of precipitation. The multi-sensor QPE in the NMQ system makes use of advantages of each observing systems and produces integrated precipitation products. The radar data in the optimal sampling area are used to dynamically calibrate the satellite infrared field and to produce a satellite QPE product. The satellite QPE is combined with radar-based QPE to fill in regions with poor radar coverage and a blended radar-satellite QPE is generated. The rain gauge data are then used to adjust the magnitude of the radar-satellite blended QPE field and to remove bias. This paper presents an overview of the NMQ multi-sensor QPE schemes currently running nationally in real time and some initial performance results.