A method to analyze the grasping and pattern force of Electromyography (EMG) simultaneously was proposed, in order to solve the problem that most myoelectric survey focused only on pattern recognition regardless of the combination of grasping pattern and force. First, surface EMG signals were collected through 4 EMG electrodes. Force data was obtained by Force Sensor Resistor (FSR). Then, the Linear Discriminant Analysis (LDA) method was used to realize pattern recognition and Artificial Neural Networks (ANN) was applied to estimate force. 4 types of EMG-force relationship were built in 4 different grasping modes. Once the grasping pattern identified, the program called the corresponding force model to estimate force value and achieved the combination force decoding and pattern recognition. The experimental results illustrate that when pattern and force are analyzed simultaneously, the average classification accuracy is about 77.8%; meanwhile the force prediction accuracy rate is about 90%. The proposed method can be applied to myoelectric control of the prosthetic hand, not only the user's intension of grasping mode can be decoded, but also the desired force can also be estimated. The stable grasping can be assisted by this approach.