Automatic Language Identification represents an important task for improving many real-world applications such as opinion mining and machine translation. In the case of closely-related languages such as regional dialects, this task is often challenging. In this paper, we propose an extensive evaluation of different approaches for the identification of Italian dialects and languages, spanning from classical machine learning models to more complex neural architectures and state-of-the-art pre-trained language models. Surprisingly, shallow machine learning models managed to outperform huge pre-trained language models in this specific task. This work was developed in the context of the Identification of Languages and Dialects of Italy (ITDI) task organised at VarDial 2022 Evaluation Campaign. Our best submission managed to achieve a weighted F1-score of 0.6880, ranking 5th out of 9 final submissions.