The hybridization of cocoa generates new varieties with the aim of opening new flavors, textures, and disease resistance. The objective of this study was to develop and validate classification models based on NIR hyperspectral imaging and chemometrics for the discrimination of five valuable cocoa bean hybrids. The chemometrics tools, PLS-DA and SVM, showed comparable results for two-class (hybrids) models, but SVM (3.8–23.1% prediction error) was superior to PLS-DA (4.4–34.4% prediction error) when all five classes (hybrids) were included in a model. PLS-DA maps showed a simple and informative way to discriminate hybrids, allowing a correct classification in 50–100% of cases. Finally, it can be concluded that the models created in this work could be a good and reliably alternative to the actual visual method for the discrimination of cocoa bean hybrids.