In this work, we investigate an application of convolutional neural networks (CNNs) using radar signal for recognizing objects and human movements. We suggested 7 scenarios and collected 2000 samples of each one: nothing, a carton, a plastic box inside the carton, a metal plate inside the carton, a man sitting behind the carton, a standing man and a moving man. The numeric radar data is preprocessed to generate image. Then it is sent to a pre-trained CNN (Inception V3) for feature extraction. The model is trained for 30 epochs in a batch of 20 samples. We choose to fine-tune the top 2 inception blocks by training the model once again. After all this training process, it attains validation accuracy of 99.2% and testing accuracy of 99.7%. The model carries through the classification of these 7 scenarios.