Abstract In this chapter we introduce a special kind of neural network known as a self-organizing map (SOM) and use it to cluster/classify pitch angle-resolved particle flux data obtained by instruments onboard satellites orbiting the Earth. As an example of the technique, we employ electron flux data at both relativistic and subrelativistic energies provided by two instruments onboard one of the twin NASA’s Van Allen Probes. For these data sets the SOM can identify the shapes of three well-known types of pitch angle distributions, and from that knowledge one can infer the associated physical mechanisms in the near-Earth space environment, particularly in the Van Allen radiation belts region. The SOM-based methodology can be used with multiplatform spacecraft data, thus enabling a prompt characterization of the physical processes throughout the Earth’s magnetosphere. The steps required to apply our neural network-based approach to pitch angle-resolved particle flux data from any spacecraft mission are laid out.