There are two ways to interpret your question:

• Does CART® allow multi–class targets (eg, a class label with values 1,2,3,...etc)

  CART has been used in real world classification problems with more than 400 classes.

  In one project our goal was to predict which specific model of new car a given person actually bought. In the project there were more than 400 different car models available and the predictors were drawn from a lengthy set of attitude and interest questions.

  For such models to be useful you need to have a decent sample size for each level of the target. In the car purchase study some models had been bought by more than 2000 people (a good sample size) while some exotic and expensive cars had been bought by fewer than 10 people (the total sample size was over 50,000 records). Naturally, we could not place much faith in the predictions concerning the least frequently bought cars. However, overall, the models built were both quite accurate and generated considerable insight into the factors influencing consumer choice in car purchases.

The Salford CART decision tree is exceptional in supporting an essentially unlimited number of target levels. Of course the vast majority of classification problems tackled by analysts have two classes, or are reformulated to have two classes. There is no reason, however, to confine yourself to just two levels if you are working with CART. In our training materials we discuss three–level, five–level, and ten–level examples in detail.  The ten–level example concerns the reverse engineering of a clustering solution, in which a market researcher was looking to extract a simple set of rules that could be used to assign new records to a previously constructed clustering solution based on a very large number of variables. Ten levels is a rather small number when considering how far you might be able to stretch the CART machinery. In our work with a car manufacturer our goal was to predict the specific car model chosen by a new car buyer from a set of more than 400 alternatives.  The analysis was based on survey responses to several hundred attitude and preference questions administered to more than 20,000 new car buyers, and the results yielded extraordinary insight into the needs and wants driving ultimate car model selection. In our own internal testing of CART classification based on synthetic data, we have successfully run CART models on targets with 1,000 levels.