Spectroradiometer data (350 – 2500 nm) were acquired in late summer 1999 over various forest sites in Appomattox Buckingham State Forest, Virginia, to assess the spectral differentiability among six major forestry tree species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), shortleaf pine (Pinus echinata), scarlet oak (Quercus coccinea), white oak (Quercus alba), and yellow poplar (Liriodendron tulipifera). Data were smoothed using both moving (9-point) and static (10 nm average) filters and curve shape was determined using first and second differences of resultant data sets. Stepwise discriminant analysis decreased the number of independent variables to those significant for spectral discrimination at -level of 0.0025. Canonical discriminant analysis and a normal discriminant analysis were performed on the data sets to test separability between and within taxonomic groups. The hardwood and pine groups were shown to be highly differentiable with a 100% cross-validation accuracy. The three pines were less differentiable, with cross-validation results varying from 61.64% to 84.25%, while spectral separability among the three hardwood species showed more promise, with classification accuracies ranging from 78.36% to 92.54%. The second difference of the 9-point weighted average filter was the most effective data set, with accuracies ranging from 84.25% to 100.00% for the separability tests. Overall, variables needed for spectral discrimination were well distributed across the 350 nm to 2500 nm spectral range, indicating the usefulness of the whole wavelength range for discriminating between taxonomic groups and among species. Derivative analysis was shown to be effective for between and within group spectral discrimination, given that the data were smoothed first. Given the caveat of the limited species diversity examined, results of this study indicate that leaf-on hyperspectral remotely sensed data will likely afford spectral discrimination between hardwoods and softwoods, while discrimination within taxonomic groups might be more problematic.