Identifying plant species requires considerable knowledge and can be difficult without complete specimens. Fourier-transform near-infrared spectroscopy (FT-NIR) is an effective technique for discriminating plant species, especially angiosperms. However, its efficacy has never been tested on ferns. Here we tested the accuracy of FT-NIR at discriminating species of the genus Microgramma. We obtained 16 spectral readings per individual from the adaxial and abaxial surfaces of 100 specimens belonging to 13 species. The analyses included all 1557 spectral variables. We tested different datasets (adaxial + abaxial, adaxial, and abaxial) to compare the correct identification of species through the construction of discriminant models (Linear discriminant analysis and partial least squares discriminant analysis) and cross-validation techniques (leave-one-out, K-fold). All analyses recovered an overall high percentage (> 90%) of correct predictions of specimen identifications for all datasets, regardless of the model or cross-validation used. On average, there was > 95% accuracy when using partial least squares discriminant analysis and both cross-validations. Our results show the high predictive power of FT-NIR at correctly discriminating fern species when using leaves of dried herbarium specimens. The technique is sensitive enough to reflect species delimitation problems and possible hybridization, and it has the potential of helping better delimit and identify fern species.