In this experiment, which is based on a cohort of 44 Lipizzan mares from the Austrian state stud farm of Piber, we present new statistical techniques for the analysis of shape and equine conformation using image data. In addition, we examined which strategies and procedures of image processing techniques led to a successful interpretation of the traits implemented in horse breeding programs. A total of 246 two-dimensional anatomical and somatometric landmarks were digitized from standardized photographs, and the variation of shape has been analyzed by the use of generalized orthogonal least-squares Procrustes (generalized Procrustes analysis (GPA)) procedures. The resulting shape variables have been regressed on the results from linear type trait classifications. In addition, the rating scores of six conformation classifiers were tested for agreement, yielding an inter-rater correlation (inter-class correlation) ranging from 0.41 to 0.68, respectively, a κ coefficient ranging from 0.16 to 0.53. From the 12 linear type traits assessed on a valuating scale, only the type-related traits (type, breed-type and harmony) revealed significant (P<0.05) results in the regression analysis of shape variables on linear type traits. The other nine traits were characterized by a lower agreement between classifiers and did not result in a significant "shape regression". Finally, the "horse shape space" defined by shape variables resulting from GPA procedures offered the possibility to assist in trait definition and in the evaluation of ratings, and it is an adequate biological and objective scale to human perception of conformation, which is expressed in numerical data only.