Nystagmus describes an involuntary oscillation of one or both eyes. Depending on its origin, it decreases visual acuity and may lead to vertigo, oscillopsia, physical strain, and social anxiety. Available assistive technology only accounts for the limited sight, rather than compensating the nystagmus itself. When designing and using a system for nystagmus compensation, it is necessary to create a user model that covers the user’s nystagmus as detailed as possible, due to the various specific manifestations of the illness. Determining nystagmus parameters involves a time-consuming manual inspection of eye movement recordings and requires considerable expertise. So far, no algorithm for automatic nystagmus parameter detection and categorization of eye movements has been established. Therefore, the here presented paper strives to address this gap by presenting a novel approach for automatic nystagmus parameter determination and real-time eye movement categorization. The algorithm for automatically determining nystagmus parameters is evaluated through a pilot study with subjects from the target group, focusing on the accuracy of parameter detection.