Maritime surveillance and monitoring systems are crucial in coastal security and resource management. Vessel recognition and identification are key tasks. However, visual inspection is a costly and labour-intensive process. This study compares methods for an automated approach for vessel identification using digital image processing. The performance of classical and Machine Learning-based feature extraction methods is evaluated and compared using a maritime vessel dataset to verify their ability to identify different vessels. The results show that BEiT-v2 achieves the highest identification performance with a mean Average Precision (mAP) of 95.05%. VGG-19 offers the best balance between accuracy (second-highest mAP) and computational cost. These findings suggest that Machine Learning methods are valuable for vessel identification, with the optimal choice depending on the specific needs of the application.