Deep learning-based robotic grasping systems rely on vast quantities of labelled data for training, driving the need for synthetically generated data for training models. However, due to their black-box nature, understanding why grasp predictions succeed or fail is challenging. This work investigates the decision-making process of a state-of-the-art (SOTA) grasping model, GraspNet, on point cloud input data using explainable artificial intelligence (XAI) methods. Three methods were implemented: Feature-Based Importance (FBI), Local Interpretable Model-agnostic Explanations (LIME) and a novel correlation-based importance approach. Each method was adapted for the GraspNet model and tested on a point cloud image of household objects placed on a surface in a cluttered manner. Results show that FBI highlights discontinuities in the point cloud, while LIME performs inconsistently due to variations in the model’s point sampling. The correlation-based method provides the most intuitive insights, but its accuracy was limited due to the non-linearities present in the model’s feature-extraction from the point cloud. These methods provide a framework for future analysis of models trained on synthetic data, aiming to help quantify the sim-to-real gap and support the development of accurate and interpretable robotic grasping systems for dynamic industrial environments.