This paper proposes a statistical model of functional landmarks delimiting low level visual areas which are highly variable across individuals. Low level visual areas are first precisely delineated by fMRI retinotopic mapping which provides detailed information about the correspondence between the visual field and its cortical representation. The model is then built by learning the variability within a given training set. It relies on an appropriate data representation and on the definition of an intrinsic coordinate system to a visual map enabling to build a consistent training set on which a principal components analysis (PCA) is eventually applied. Our approach constitutes a first step toward a functional landmark-based probabilistic atlas of low level visual areas.