Small amount of sand in oil pipelines can result in significant erosion in a very short time period. This Produced sand is a serious problem in many production situations. Installation of a system to monitor and quantify sand production from a well would be valuable to assist in optimizing well productivity and to detect sand as early as possible. We present a multi-sensor data fusion framework for sand detection. The framework is designed to collect data from oil pipeline using acoustic sensors (SENACO AS100) and Flow Analyzer (MC-II) in real time. The purpose of data fusion is to estimate the output from the acoustic sensors to determine the sand production rate. The framework combines two modules: a wireless receiving and transmission (ReT) module and data fusion module (DaF). The ReT module implementation is based on TinyOS and Crossbow MICAz motes. DaF module is implemented using Information Filter. A test bed was established from ten acoustic sensors in order to collect real data. The flow rate was monitored as well in order to collect the data with the same flow rate. The experimental result shows that the fusion method improves the system results.