The early detection of cancers is critical with respect to treatment and patient survival. Biopsy techniques that are currently employed for such diagnoses are invasive, time consuming and costly. A Terahertz (THz) imaging system potentially provides a fast and non-invasive way to detect and diagnose cancer. While there is proof of concept that THz can distinguish cancerous and normal tissue, the mechanisms underlying this differentiation are not well understood. A better understanding of THz spectral data can be gained through computational pattern recognition and related multivariate statistical tools. These allow for the differentiation of data into discrete and disjoint groups. Such separation of THz spectral data can provide complex information about diseased tissue, which can be used as a tool for distinguishing cancerous from non-cancerous cells as well as, discriminating between cancers at various developmental stages and, between different types of cancer.