Development of more efficient (rapid) and cost‐effective methodologies are needed in soil survey to meet the demand for quantitative data in digital soil mapping and updates. The objective of this study was to pilot the application of mid‐infrared (MIR)‐diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) in a soil survey field office where soil samples are processed, MIR spectra are acquired, and predictions are obtained using calibration models developed and validated from the Kellogg Soil Survey Laboratory spectral library. Mid‐infrared models were built for total C, organic C, CaCO₃ equivalent, total clay, cation exchange capacity, 1500 kPa water, and pH in water and CaCl₂ for Mollisols of the central United States. Validation results (from the MIR library) using Lin's concordance correlation (r_c) of measured versus predicted values showed that most properties predicted very well (r_c = 0.967–0.996), whereas models for total clay in B horizons and 1500 kPa water in B horizons predicted fairly well (r_c = 0.844–0.955). Models for pH predicted the least well (r_c = 0.750–0.921). The MIR‐DRS coupled with PLSR was successful in predicting soil properties for completely independent samples that were collected, processed, and MIR scanned in a soil survey field office. Predicted results using r_c ranged from 0.697 to 0.992, with pH in water having the lowest r_c and CaCO₃ having the highest r_c. All properties except pH had an acceptable level of accuracy for use in soil survey and a marginal level of acceptable accuracy for total clay. Direct calibration transfer was feasible.