This paper evaluates the classification of objects given their signal data via a simple convolutional neural network (CNN). Many of the signal processing neural networks involve sound frequency data or Doppler signatures that contain the characteristic features of each object. In this study, we use frequency-intensity data within range-time domain from a Frequency-Modulated Continuous-Wave (FMCW) radar to classify detected objects. The application of various data augmentation methods mitigated the scarcity of labeled data from our field experiments. Time stretching, frequency shifting and noise addition preserved the semantic information of each rangetime data, further improving the models ability to generalize. Modifications applied to our data, which is then converted into a low-level log-scaled mel-spectrogram representation, are learned by CNN models with a set of convolutional and max-pooling layers along with fully-connected layers and selective residual module. Based on our experiments, we conclude that raw radar data can be used for training CNNs for classification and thus can be used to classify a car, a human, and an UAV.