Reliable sand detection is an important component of oil production system. In practice, produced sand in oil pipelines poses a serious problem in many production situations, since a small amount of sand in the produced fluid can result in significant erosion in a very short time stage. A new data fusion framework for sand detection in pipeline is presented. The framework is collecting data from oil pipeline using acoustic sensors (SENACO AS100) and Flow Analyzer (MC-II) in real time. The framework combines two modules: a wireless receiving and transmission (ReT) module and a data fusion module (DaF). The ReT module implementation is based on TinyOS and Crossbow MICAz motes. In order to optimize between the complexity and accuracy needs, DaF module is implemented using two methods; Fuzzy Art (FA) and Maximum Likelihood Estimator (MLE). The results show the efficient number of sensors needed and compare between FA and MLE redundant.