Handheld, vehicle mounted and air-borne Ground Penetrating Radar (GPR) systems have been identified as potential technology solutions for detection of current and evolving buried threat objects. However, the success rate of the GPR systems are limited by operational conditions and the robustness of automatic target recognition (ATR) algorithms embedded with the systems. With the ever-increasing complexity of target configuration and their deployment scenarios it is becoming a challenge to develop ATR algorithms robust enough to detect and identify GPR signatures of a wide variety of threat objects. The aim of this research is to design a potential solution for detection of threat objects using GPR data and reducing the number of false alarms. In this paper, a Machine Learning (ML) based ATR algorithm applicable to GPR data is developed to detect complex patterns and trends relevant to a multitude of threat objects. The proposed ATR algorithm has been validated using a data set acquired by a vehicle mounted GPR array. The data set utilized in this investigation involved GPR data of threat objects (both conventional and improvised) commonly found in realistic operational scenarios. Lane based summaries of the algorithm performance are presented in terms of the probability of detection threat objects and false alarm rate. Preliminary results of the proposed ML techniques have shown promise of achieving a high detection rate and a low false alarm rate in multiple GPR data sets collected in challenging geographical locations.