Aim of this study is the easy and low-cost classification of basic hand grasp motions. For this aim, two sEMG sensors were placed on the forearm muscles. The reason is that to place the sensors on two forearm muscles is a low-cost and easy method. Also these forearm muscles are related with grasp motions. At first stage of the study, feature extraction methods were applied to the energy values of two channel signals. The feature extraction methods used at first stage were the filtering and histogram calculation. Then, the relations (correlations) between the histogram values were calculated by concordance correlation method. Finally, the concordance correlation values were used to classifier as input. The cascaded-structure classifier was used to obtain best results. According to the classification results, the average success rate calculated as 94.72%. Based on this high success rate, the method used in the study is proposed to some medical decisions, such as detection of muscle disorders or graspmuscle relation.