The majority of advanced neuroimaging studies implement group level analyses contrasting a group of patients versus a group of controls, or two groups of patients. Such analyses may identify for example changes in grey matter in specific regions associated with a given disease. Although such group investigations provided key contributions to the understanding of the pathological process surrounding a wide range of diseases, they are of limited utility at an individual level. Recently, there is a trend towards individual classification analyses, representing a fundamental shift of the research paradigm. In contrast to group comparisons, these latter studies do not provide insights on vulnerable brain areas but may allow for an early (and ideally preclinical) identification of at risk individuals in routine clinical setting. One currently very popular method in this domain are support vector machines (SVM), yet this method is only one of many available methods in the field of individual classification analyses. The current manuscript reviews the fundamental properties and features of such individual level classification analyses in neurodegenerative diseases.