Ideally echoes in radar reflectivity data correspond to precipitating particles, however they do not, and as a result, automated weather radar products that use these data are drastically affected when conditions are not ideal. Weather radar data of the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) is one such case that often suffer contamination, in particular by electromagnetic interference and the identification and mitigation of interference echoes is an ongoing problem in radar meteorology in these regions. In order to improve the quality of the data and consequently the automated products, especially for the radar quantitative precipitation estimation (QPE), a fuzzy logic algorithm is applied upon the radar reflectivity data to provide a probability guidance for segregating interference-contaminated echoes from precipitating echoes. Specifically, adequate features to highlight interference characteristics are required for the algorithm to be effective based on prior experiences. This approach is presented in this study to derive membership functions and their relatively objective weights are determined based on the superior result of sensitivity test from interference cases. The result of which produced a value that quantifies the possibility of each bin being affected by interference. Cases that highlighted the interference were examined and demonstrated the ability of the fuzzy logic approach to remove interference echoes from radar reflectivity map. Moreover, the presented method can be feasibly implemented in real-time multi-radar operations as a quality control (QC) aid.