Mineral resource classification is essential because it is required for public disclosure. The resources must be binned by a qualified person into measured, indicated and inferred categories using an appropriate methodology. The classification methodologies used have long been a topic of research and discussion. There are various traditional and geostatistical methods used in practice. Some are more popular than others. Because of the uniqueness of each ore deposit, it seems almost impossible to have a single industry-accepted norm for mineral resource classification. Therefore, some experts have adapted the methodologies tailored for the deposits they are working on, but many simply use the methods they and their clients understand and are comfortable with. In this paper, a new approach is presented to classify the mineral resources using a data geometry index. This index combines both traditional and geostatistical aspects of resource classification based on the data configuration around the block interpolated. It is a function of several variables such as kriging variance calculated for a block, distance from the block to the nearest sample and the average distance of all samples used to interpolate the block. Thus the approach using this index takes into account the spatial data configuration around the block being estimated, as well as the traditional distance criteria. The data geometry index and how it is calculated is explained. A case study for a gold deposit is presented to see how the classification based on this index compares to the existing methodology used in this deposit for mineral resource classification.