Fifteen typical features in time domain, time-frequency domain and non-linear dynamic are extracted from the mechanomyogarphy (MMG) signals in neck muscles. They are divided into five feature sets according to their nature, and part of them are constructed to high-dimension feature vectors before reducing the dimension by principal component analysis (PCA), which are applied in the pattern research for head movements. The MMG of six head movements (forward, backward, swing to left, swing to right, turn to light, turn to right) are classified by adopting three sorts of classifiers, which are support vector machine (SVM), K nearest neighbor (KNN) and linear discriminant analysis (LDA). Experimental results show that selecting the method of combining features in time domain, time-frequency and non-linear dynamic, and adopting SVM as the classifier can improve the classification accuracy up to higher than 80% in each movement, thus acquiring relatively higher rate.