Radar systems have been increasingly used to monitor birds. To take full advantage of the large datasets provided by radars, researchers have implemented machine learning (ML) techniques that automatically read and attempt to classify targets. Here we used data collected from two locations in Portugal with two marine radar antennas (VSR and HSR) to apply and compare the performance of six ML algorithms that are widely used in the literature: random forests (RF), support vector machine (SVM), artificial neural networks (NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and decision trees (DT), all trained with several dataset configurations. We found that all algorithms performed well (area under the receiver operating characteristic (AUC) and accuracy > 0.80, P < 0.001) when discriminating birds from non-biological targets such as vehicles, rain or wind turbines, but greater variance in the performance among algorithms was apparent when separating different bird functional groups or bird species (e.g. herons vs. gulls). In our case study, only RF was able to hold an accuracy > 0.80 for all classification tasks, although SVM and DT also performed well. Further, all algorithms correctly classified 86% and 66% (VSR and HSR) of the target points, and only 2% and 4% of these points were misclassified by all algorithms. Our results suggest that ML algorithms are suitable for classifying radar targets as birds, and thereby separating them from other non-biological targets. The ability of these algorithms to correctly identify among bird species functional groups was found to be much weaker, but if properly trained and supported by a good ground truthing dataset, targeted to the relevant species groups, some of these algorithms are still able to achieve high accuracies in classification tasks. Such results indicate that ML algorithms are suitable for use in near real-time monitoring of bird movements, and may help to mitigate collision of birds with, for example, wind turbines or airplanes.