In this paper, an active fault-tolerant control scheme is developed for nonlinear batch processes with sensor faults. This scheme contains three modules: a fault detection and isolation (FDI) module, an output estimation module, and a controller. A batchwise neural-network-based FDI and a batch-wise neural-network-based soft sensor are proposed as the first and second modules, respectively, together with an iterative learning controller. In the nominal case, the measured output is used in the iterative learning control. After a fault is detected and isolated, the estimation produced by the soft sensor is used. The FDI and estimation modules are not modelbased; therefore, most types of sensor faults can be addressed. To illustrate the effectiveness and practicability of this method, two examples are given: the first one is simulation study on a three-tank system, and the second one is an experimental application on the injection molding process.