The main goal of this research is to examine some new methods for the automatic clustering of hyperspectral data. Hyperspectral data consists of images which originate from the same physical phenomena at various wavelengths. Use of this data is common not only for medical processing but also for military purposes. In this unique research we will analyze hyperspectral data which has been taken of different types of events that evolve both temporally and spectrally. These events would seemingly be indistinguishable if only the spectral or the temporal dimensions were used. By exploiting the unique attributes of the hyperspectral temporal data, we show that we can significantly improve our target assignment capabilities. We will develop methods to evaluate our ability to correctly assign these events from each other. We will discuss how to automatically cluster such events and determine how many different types of events actually exist. Practical problems previously discussed in the literature will be demonstrated.