We describe an algorithm to dynamically classify flow cy- tometry data samples into several classes based on their im- munophenotypes. Flow cytometry data consists of fluores- cence measurements of several proteins that characterize dif- ferent cell types in blood or cultured cell lines. Each sample is initially clustered to identify the cell populations present in it. Using a combinatorial dissimilarity measure between cell populations in samples, we compute meta-clusters that correspond to the same cell population across samples. The collection of meta-clusters in a class of samples then de- scribes a template for that class. We organize the samples into a template tree, and use it to classify new samples into existing classes or create a new class if needed. We dynam- ically update the templates and their statistical parameters as new samples are classified, so that the new information is reflected in the classes. We use our dynamic classifica- tion algorithm to classify T cells that on stimulation with an antibody show increased abundance of the proteins SLP- 76 and ZAP-70. These proteins are involved in a platform that assembles signaling proteins in the immune response. We also use the algorithm to show that variation in an im- mune subsystem between individuals is a larger effect than variation in multiple samples from one individual.