The problem of dimension reduction is a popular problem that arises in practice and it is usually combined with the concept of collinearity. In this work we proceed with a proposal of a new dimension reduction technique that can be considered as a rotated Principal Component Analysis, to be referred to as, Principal Rotation Analysis (PRA). With the use of this technique, we can generate independent features that each encloses the variability of the original features and simultaneously create clusters of significance.