TRISO (tri-structural isotropic) fuel is anticipated to be one of the fuel types for future small modular reactors. A TRISO-fuelled pebble in a pebble bed reactor (PBR) consists of thousands of TRISO fuel particles embedded inside a graphite sphere. The diameter of each of the fuel particles is approximately 1 mm. A PBR can contain hundreds of thousands of pebbles, each with a diameter of approximately 6 cm. Unlike in traditional reactor designs, which have a relatively small number of discrete fuel assemblies, the large number of pebbles inside a PBR makes the tracking of individual fuel pebbles difficult. The distribution of TRISO particles within the pebble provides a unique fingerprint that identifies a pebble and can be used to track it. This fingerprint can be determined using a standard X-ray radiograph or a more time-consuming X-ray computed tomography scan. When an individual radiograph is used, the pebble image is compared to a library of reference images of the pebbles. To obtain a good match, the orientation of the pebble must be close to the orientation used for the reference image. As a pebble exits the reactor in a random orientation, the pebble must be reorientated before its identification can take place. This paper discusses the addition of a reference particle in the non-fuelled region of the pebble, as well as the simultaneous acquisition of pairs of radiographs at 90° with respect to each other, to aid pebble reorientation and the development of an identification framework. The framework follows a hybrid approach in which the pebble is first reorientated using machine learning techniques before being identified using traditional computer vision techniques. Other effects such as the impact of transmutation of the elements inside the fuel and the radioactivity of irradiated pebbles are also discussed.