In this paper, we propose an Explainable Artificial Intelligence methodology to interpret point cloud classification models. Our approach uses Shapley values from cooperative game theory which assigns contribution values to individual points towards a classification. Point cloud data contains rich spatial information, therefore it requires sophisticated deep learning models to make accurate predictions. However, these models often lack interpretability. Our methodology, Shapley-based Point Attribution eXplanations (SPAX), addresses this issue by quantifying and visualising the contribution of each point in a point cloud towards a prediction. To make it computationally feasible, a Monte Carlo approximation is used to estimate the Shapley values. To evaluate the method, a PointNet classifier trained on the ModelNet10 dataset is used to determine the Shapley values. These values are then colored according to their magnitude and used to visually interpret their impact on the classification. The results show how spatial features become more interpretable because they identify key components to determine classification results. This study creates foundational principles to enable better point cloud interpretation, which produces opportunities to boost real-world deployments of transparent algorithmic systems.