In this paper, human motion classification using multilayered neural network is proposed to classify motion signal based on vertical ground resultant force (VGRF). VRGF readings were acquired using an instrumented treadmill. The work presented in this paper seeks to classify six activities i.e. standing to walking, walking, walking to jogging, jogging, jogging to running and running, based on the measured VGRF. The data set involved 229 healthy Asians aged between 20 and 24, yielding a total of 740 activity classes. All activities varied as a result of subjects' desired speed. However, it was observed that the VGRF of the last five strides reaction forces was sufficient to achieve 83% classification rate for the training set and 73% for testing set. The influence of number of hidden neurons was also analyzed to obtain optimal classification performance.