In this paper, multi-directional 2DPCA is employed for face recognition of two different databases. All face images are rotated and their two dimensional principal components are calculated as features as features of facial images. These features in various directions are fused to form features of an individual's facial image. The results of this technique are compared over FERET database and an in house self-made database. For FERET database, MDi2DPCA is giving 82.3% result and for self-made database it is giving 91.67% result.