This work deals with acoustic scene classification on a dataset published in the DCASE2017 challenge. The goal is to achieve better performance than the performance presented in the challenge, using neural networks and mel-spectrogram features. We present the processing of the dataset, the classifier and models, and the selected hyperparameters. The best performance was obtained using mel-spectrogram features, an EfficientNet V2 S neural network, and a MiniNet net as selection algorithm. Accuracy of 83.33% was achieved, which is higher than the performance to which we compare the results.