It has become increasingly common in academic and industrial environments the necessity to process huge amounts of seismic signals. Several researchers have been seeking for ways to improve and optimize the processing of these enormous amounts of data that are related to routine demands of geophysicists. One of these demands is the classification of distinct seismic regions captured by the same seismograph, a task that could take up to months of manual data processing. In this paper, we propose the usage of machine learning techniques to the task of the classification of seismic regions, in order to achieve accurate results with better performance and speed. The algorithms K-NN, MLP, Naive Bayes and Decision Tree were used for tests as base classifiers and also combined on ensemble methods. We also employed Deep Learning techniques, namely, a pure RNN network, and a variation of RNN called LSTM. The best results were achieved when using heterogeneous classifiers, showing accuracy rates of up to 98.52%. The results show that one can build an efficient seismic region classification system even when few classified data are already available for a specific seismograph setting.