Yes it is.
For multiclass classification problems, you can use 2 strategies: transformation to binary and extension from binary.
In approaches based on transformation to binary, you have:
- OVA (one versus all), which is based on training k binary classifiers (k = #classes), where the i-th classifier is specialized on distinguishing the i-th class from all the other k-1 classes.
- OVO (ove versus one), which is based on training k * (k-1) / 2 classifiers, where each classifier learns to distinguish 2 classes only. When a prediction is required, each clasisfier votes on the class it thinks it's correct, and the class with more votes is selected as the output.
On the other hand, you have extension from binary approaches: some classification algorithms are already capable of dealing with these multiclass problems. Some examples: kNN, decision trees, naive bayes...
You can find a bunch of resources on this.
For more practical purposes, please check out the following resource: https://scikit-learn.org/stable/modules/multiclass.html