In security surveillance at the perimeter of critical infrastructure, such as airports and power plants, approaching objects have to be detected and classified. Especially important is to distinguish between humans, animals and vehicles. In this paper, micro-Doppler data (from movement of internal parts of the target) have been collected with a small radar. From time-velocity diagrams of the data, physical features have been extracted and used in a Boosting classifier to distinguish between the classes "human", "animal" and "man-made object". This type of classifier has received much attention lately, but not in radar micro-Doppler classification. The classification result on the current data reaches 90% correct classification with this classifier. The ability to distinguish between humans and animals is good on this data. This classifier type gives insight into the classifier and the utilized features, and is easy to use. A comparison with a SVM (Support Vector Machine) classifier, which is common for micro-Doppler, has also been performed. © 2017 Institution of Engineering and Technology. All rights reserved.