Statistical characteristics of spatial properties of microstructures (particle density, volume fraction, orientation, etc.) can be obtained by means of stereological methods. Unfortunately, these methods are useless in the case of more advanced analysis, where precise, really 3-dimensional information on particle localization, connectivity, size and shape is required.

The above-described characteristics can be obtained only from 3-D images that can be interpreted as piles of 2-dimensional digital images. Such data sets are currently obtained in most cases from X-ray microtomography. Their subsequent analysis faces several problems that can be divided into three groups:
1. Very high memory and computational power requirements. For example, 2-D gray-scale image of size 512x512 pixels occupies 257 kB, whereas the 3-D image of size 512x512x512 voxels occupies 134 MB of memory.
2. More complicated image processing and lack of theory for some operations, for example skeletonization. Consequently, algorithms prepared and tested for 2-D images cannot be directly adapted for 3-D processing.
3. Difficult visual verification of the results that is routine control tool in the case of 2-D images. Various rendering techniques as well as virtual sections through 3-D images are used to display the results.

The potential and difficulties accompanying 3-D analysis are presented on the basis of several examples:
1. Polymer foam - restoration and characterization of cellular structure.
2. Metallic foam with open porosity - examples of problems with construction of 3-D skeletons
3. Metal matrix porous composites - characterization of 3-D structure of pores
4. Glass fiber epoxy matrix composite - analysis of the spatial distribution of fibers.

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