The detection of potentially threatening nuclear materials is a challenging homeland security problem. This research reports on the application of a novel statistical relational learning algorithm, Higher Order Naïve Bayes (HONB), to improve the detection and identification of nuclear isotopes. When classifying nuclear detection data, distinguishing potentially threatening from harmless radioisotopes is critical. These also must be distinguished from the naturally occurring radioactive background. This research applied Higher Order Learning to nuclear detection data to improve the detection and identification of four isotopes: Ga67, I131, In111, and Tc99m. In the research traditional IID machine learning methods are applied to the area of nuclear detection, and the results compared with the performance of leveraging higher-order dependencies between feature values using HONB. The findings give insight about the performance of higher-order classifiers (described in ) on datasets with small numbers of positive instances. In the initial study, Naïve Bayes was compared with its higher-order counterpart, Higher Order Naïve Bayes. HONB was found to perform statistically significantly better for isotope Ga67 when using a preprocessing methodology of discretizing then binarizing the input sensor data. Similar results were seen for different amounts of training data for I131, In111, and Tc99m. HONB was also found to perform statistically significantly better for isotopes I131 and Tc99m when the preprocessing involved normalization, discretization then binarization. This study shows that Higher Order Learning techniques can be very useful in the arena of nuclear detection.