This study presents an orientation independent imaging technique for the classification and recognition of blood cells with relevant applications to related problems in cytometry and medical diagnosis. The proposed method integrates three important aspects towards its practical implementation: 1. The use of the principal component (PC) transform to reorient in an optimal fashion the image data and to make of the feature matching an orientation independent process; 2. The establishment of similarity measures to quantify the matching process of the shape of the blood cell populations with any chosen degree of certainty; and 3. The application of the recognition and classification processes once all the similarity measurements are gathered. An extension of this two-dimensional (2-D) method to a three-dimensional (3-D) base is proposed. Results of this technique using real-world image data of blood cell populations are given. It is appropriate to note that although the images used here are real-world samples of blood cell populations, other samples containing different biological specimens could have been used just as well.