The recent availability of complete cell lineages from live imaging data opens the way to novel methodologiesfor the automated analysis of cell dynamics in animal embryogenesis. We propose a method for the calcula-tion of measure-based dissimilarities between cells. These dissimilarity measures allow the use of clusteringalgorithms for the inference of time-persistent patterns. The method is applied to the digital cell lineagesreconstructed from live zebrafish embryos imaged from 6 to 13 hours post fertilization. We show that theposition and velocity of cells are sufficient to identify relevant morphological features including bilateral sym-metry and coherent cell domains. The method is flexible enough to readily integrate larger sets of measuresopening the way to the automated identification of morphogenetic fields.