Accurately identifying the soil map unit component at a specific point-location or position within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone-based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non-soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a point-location based on its numerical similarity to the soil components mapped in that area. To implement this testing framework, we used a subset of the United States National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile point dataset and the U.S. Soil Survey Geographic (SSURGO) database for querying profile data of mapped soil classes in the area surrounding each point. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of geographic space (i.e., depth-wise variably) and feature space (e.g., texture class vs. clay percentage). Three soil property groups (i.e., Novice, Expert, Expert-Plus) representing different levels of experience required for their measurement and three types of depth-support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class,rock fragment volume class, and soil color) resulted in nearly as high classification accuracy (46-53%) as that achieved with an Expert (48-57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert-Plus dataset (53-60%). A measure of management relevant accuracy was assessed using correlated Ecological Sites (ESDs) and Land Capability Classifications (LCC) which followed similar trends for the Novice (ESD: 78-82%; LCC: 70-76%), Expert (ESD: 79-84%; LCC: 71-77%), and Expert-Plus (ESD: 83-85%; LCC: 74-79%) datasets. This study also showed minimal effect from the type of depth-support used to represent depth-wise variability.