Conv Net presents an efficient tool for SAR data interpretation.In this thesis,we use augmented Open SAR dataset for training and comparing several existing deep learning models for the purpose of ship classification.All the deep learning algorithms reveal the difficulty of classifying between Cargo and Tanker ships in the dataset,Consequently CNN models designed with sparse connections between convolution layers are found to considerably improve the classification performance against the existing models due to greater regularization.Further adding depth to the network and keeping lesser trainable parameters stabilize the validation accuracy.Consequently,the CNN architecture in this research is found to outperform other models trained on Open SARShip dataset.Lastly,the hyperparameter selection approach taken to develop the final CNN leads to a F1 score of 78% for three classes and 82% when distinguishing 2 classes of ships.