This paper tackles the Human Activity Recognition (HAR) in the kitchen environment using radar as the sensing tool. The setup includes two Frequency Modulated Continuous Wave (FMCW) radars each mounted separately on the ceiling and the wall, respectively. The data collected from concurrently operated radars was used to evaluate the efficacy of the HAR. In addition, an indoor kitchen scenario in the presence of furniture is considered and the HAR is taken as a procedure to detect common cooking-related activities by a single human subject where a machine learning model is developed to address the HAR as a multi-class classification problem. Our experimental results show the superior performance of the proposed method in detecting kitchen activities, especially, when the features from both the radars are being fused in the central processor.1