The increasing scale of plantation and production of foxtail millet (Setaria italica) has led to a strong demand to identify its varieties easily and quickly. It is also important for researchers to find, screen, identify, protect, and collect new mutant species and germplasm resources of foxtail millet in the early stage of growth. In this study, we present an innovative approach to identifying foxtail millet varieties using visible–near-infrared (VIS–NIR) spectral information from their growing leaves. Seven varieties of foxtail millet were successfully identified. Ten effective wavelengths (1440, 1660, 1775, 550, 410, 980, 1180, and 462 nm) were extracted. An accurate and stable prediction model for foxtail millet varieties was developed using the backpropagation (BP) neural network coupled with principal component analysis (PCA). The model can completely classify the foxtail millet varieties with a minimal number of five hiddenlayer nodes. Its predictive correlation coefficient (Rv) is as high as 0.9994. Accordingly, the root-means-square error of prediction (RMSEP) and the standard error of prediction (SEP) are both 0.0026. The results show that the VIS–NIR spectral technique can be used for identifying foxtail millet varieties.