Designers, engineers and ergonomists are seeking to exploit the opportunities offered by the 3D anthropometric technologies. These technologies make 3D measurements possible and provide us with a more detailed description of human body in comparison with the traditional 1D or 2D data processing. In many industrial design cases, there is a need to take into consideration various postures of the human body when the product is designed. This thesis presents an approach to transforming measured body data between various postures. In this research the measured human body data were substituted by a proper set of landmarks. This data set was used as a basis of transforming the specific body postures. Artificial neural networks have been used for the actual conversion of data. The input consisted of a set of demographic data and the set of coordinates of the landmarks characterizing a given posture. The output was another set of landmarks describing the transformed posture. The results have showed that the ANNs-based and landmark-based posture prediction technology is computationally effective. On the other hand, it needs to be further developed in order to properly consider the specialties of different user groups. This posture prediction technology is generally applicable and opens up new possibilities in studying, for instance, human motions and hand postures.