The aim of this study is to design an artificial neural NETWORK (ANN) to model length–tension RELATION in skeletal muscle isometric contraction. We obtained the data set, including physiological and morphometric parameters, by myography and morphometric measurements on frog gastrocnemius muscle. Then, we designed a multilayer perceptron ANN, the inputs of which are muscle volume, muscle optimum length, tendon length, and preload. The output of the ANN is total tension. The experimental data were divided randomly into two parts. The first part was used to train the ANN. In order to validate the model, the second part of experimental data, which was not used in training, was employed to the ANN and then, its output was compared with Hatze model and the experimental data. The behavior of ANN was similar to experimental data and Hatze model. This means ANN model can be used as a proper simulate in isometric contraction essays. Our results indicate that ANNs represent a powerful tool to capture some essential features of muscle isometric contraction.