Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise, a task typically completed by human analysts. This process has usually been done by experts labeling each waveform visually, which is time-consuming and tedious for large datasets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, k-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show the two trained models agree with labels from human analysts but required only 0.4% time. Although the quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our analyses demonstrate the capability of the automated processing using these two machine learning models to reduce outliers in surface-wave-related measurements without human quality control screening.