An inter-comparison of ground radar reflectivity with space-borne TRMM’s Precipitation Radar using alignment methodology has been presented. For this purpose, reflectivity data from Dual Polarization Ground Radar (GR) maintained by the India Meteorological Department (IMD) at the IMD Delhi site is utilized. IMD Delhi has collected radar data during Continental Tropical Convergence Zone (CTCZ) programme from 2011 to 2013. The present study utilizes monsoon data collected during 4 months, namely, June, July, August, and September (JJAS) from the year 2013. The GR observables are first converted from polar coordinates to Cartesian coordinates and then spatially aligned with TRMM PR data at a near-real-time to a common volume. It was found that in all the overpass cases, IMD’s GR reflectivity has a positive bias when compared with TRMM PR. A methodology is proposed to ‘correct’ the GR reflectivity data by considering TRMM PR data as ‘truth’ using a neural network-based approach. A supervised learning algorithm based on the back-propagation neural network is used for this purpose. Ground radar reflectivity is fed as input to the network, while the TRMM PR reflectivity is the target. The trained network is then tested for its performance against data which is not used as part of the training process. The present methodology demonstrates the match up of uncalibrated ground radar measured reflectivity and a well-calibrated space-borne radar.