Signs there are too many class labels
A class taxonomy should:


Serve the business needs 
Be learnable


There is a potential tradeoff here. The more exact and specific the taxonomy, the more you'll know about the entities and you'll be able to server better the business needs. However, for a large taxonomy the classifier will have to model more complex rules, will have less samples for each case and the boundary samples will have more influence.

Check the number of samples per label in your dataset. A class label with not enough samples will be hard to learn. If you have many class label, the ratio of some of them will also be low, introducing an imbalance learning problem which is also harder. 

Using a cost matrix, you can evaluate the extra cost due to merging some classes. merge classes when the cost on not distinguishing among them is low.

Building a taxonomy of class might can help working in many resolutions. Take for example animals, you can create a taxonomy of animals -> reptiles -> crocodiles and then try to distinguish reptiles from other animals or crocodiles from other reptiles.

In many some cases you might get many class labels due to a Cartesian product of some dimensions. Using the animals examples again, you might be interested in dimension like "is_flying?", "is_carnivore?" and learn them instead of the animal classes. These dimensions will probably be more balanced and can be learn with a certain degree on independence (reusing samples). The down side is the a flying carnivore will be a bird of prey and not necessarily a falcon.