Does developmental anatomy have a future in the age of molecular biology and digital technologies? Specifically, will morphological characters continue to be used in comparative developmental biology, or will new types of character be defined? Traditionally, comparative embryology was a non-quantitative, 'portrait-gallery' science. Wilhelm His attempted to develop a character-based, more quantitative approach. Quantitative approaches to development have been also been suggested by Meinhardt and others. With the current availability of computing power and the growth of bioinformatics and phylogenetic methodology, quantitative methodologies are increasingly being applied to studies of embryonic development. Our aim in this article is to examine some of these approaches. In both anatomical and molecular studies, the parameters to be quantified are temporal and spatial. Temporal data are analysed by techniques, such as event pairing, that analyse developmental sequences. In this case, the characters are developmental events. Spatial information can be analysed using morphometrics, in combination with computer-assisted 3D reconstruction. In spatial analyses, anatomical parts may be used as the characters. A major challenge in the coming years is to develop techniques for analysing 3D patterns of developmental gene expression and to compare them between species or individuals. Such analyses have to be defined in relation to five dimensions: the 3 orthogonal spatial planes; time; and individuals. The difficulties of such analyses are complicated by problems of homology. Some possible solutions are suggested. For example, it may be possible to use voxels as characters, and to assign to them attributes according to gene expression domains. At first sight, it might seem that traditional morphological characters would no longer be required in comparative embryology. However, we believe that some kind of anatomical framework will always be needed in comparative biology. The interplay between classical morphological characters, gene expression patterns and computing methodologies will be an exciting area for future work.