Since the date of establishment of the SETI Institute, its scientists have used various approaches in their search for extra-terrestrial intelligence (SETI). A novel idea involved image categorisation techniques in classifying radio signals represented by 2D spectrograms. The dataset of simulated radio signals, created for classification purposes have been used in this work to train models based on neural network architectures. It is shown in this paper that combining three different models, trained on features obtained by various techniques, has a positive impact on model accuracy and performance. Features learned by a convolutional neural network (CNN), bottleneck features from existing models and manually extracted features from the spectrograms comprised the three feature sets used as training data for the combined model. It was also shown that combining different methods of spectrogram generation resulted in improving the accuracy of the final model.