A battery is a quite complex and nonlinear system comprising interacting physical and chemical processes although it seems deceptively simple. State-of-charge (SOC), a parameter to describe how much energy battery has, is a key factor in battery management and its estimation is an important and challenging task. We develop an adaptive neuro-fuzzy inference system (ANFIS) to achieve the goal. First in this paper, nonconventional input variables of the ANFIS are selected by three different correlation analysis techniques, linear correlation analysis (LCA), nonparametric correlation analysis (NCA) and partial correlation analysis (PCA). Next, the ANFIS model of five inputs and one output is presented. Takagi and Sugeno's fuzzy if-then rules are used. Then, number determination of training data pairs is discussed. Finally, hybrid learning algorithm combining the gradient method and the least squares estimate (LSE) is adopted to train the ANFIS. Predicted results obtained by the ANFIS are compared with measured results, verifying presented ANFIS. For contrast, a three-layer feedforward back-propagation (BP) artificial neural network (ANN) is presented to estimate SOC. Compared with the BP ANN model, the ANFIS obtains better prediction performance when interpolating. Comparisons of the two approaches have highlighted the potential of ANFIS in modeling and prediction of the behavior of complex nonlinear dynamic systems.