Bimodal neutron and X-ray imaging provides a robust approach for non-destructive analysis of complex materials, particularly in engineering, archaeological, and life-science specimens. Over a decade ago, our group pioneered this technique, enabling a wide range of applications and inspiring similar implementations across other beamlines. This study presents examples of possible data fusions relevant to our activities. We employed three primary fusion methods: direct bivariate histogram-based fusion, Laplacian Pyramid image fusion, and PCA-based feature-level fusion. The bivariate histogram method effectively highlighted correlations and divergences between neutron and X-ray datasets, aiding material differentiation. The Laplacian Pyramid method enhanced spatial resolution and contrast, providing detailed visual and analytical representations of material phases. The PCA-based method integrated intensity and structural information, maximizing variance representation for comprehensive material characterization. Future work will focus on integrating state-of-the-art fusion techniques, such as DeepFuse-inspired methods, to further improve the quality and applicability of bimodal imaging in engineering and archaeological contexts.