The automatic identification of human activities has become an area of interest in recent years. Identifying human activities is useful in various applications, such as through-barrier identification of intruders and noncontact monitoring of patients in hospitals. Numerous methods of human activity classification have been proposed in the past, including the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). Most research in this area thus far has utilized the Short-Time Fourier Transform (STFT) as a method of obtaining the feature vectors necessary for classification. In this paper, we propose the use of the Empirical Mode Decomposition (EMD) algorithm as an alternative approach for obtaining feature vectors from human micro-Doppler signals and utilize an SVM for classification. Since the micro-Doppler signature is unique to a specific activity, the EMD outputs can be utilized as feature vectors. By utilizing the EMD algorithm in conjunction with an SVM, binary classification of human activities have shown to yield accurate results. Because SVMs were originally developed to solve the binary classification problem, additional steps must be taken in order to extend the problem to identify multiple classes. In this paper, two methods for multi-class classification will be demonstrated and compared. The first method is the one-against-all approach and the second is a decision tree based approach. In both cases, a high degree of accuracy is achieved.