In the framework of process optimization, the use of measurements to push the plant to the economically optimal operating point has re-emerged as an active area of research. One of the ideas therein is to adapt the inputs in order to track the active constraints and push certain sensitivities to zero. There are several ways of doing this. With perturbation-based optimization, the sensitivities are evaluated by perturbation of the inputs and measurement of the cost function. However, since more measurements (typically the outputs)than just the cost function are available, the idea developed in this paper is to use both the nominal model and measured Outputs to optimize the process. This is done by extending the neighboring-extremal scheme to the case of output measurements. In the case of parametric uncertainty, and if measurement noise is negligible, the approach is shown to converge to the optimum in at most two iterations. The effect of measurement noise is also investigated. The use of neighboring-extremal output feedback for optimization is illustrated via the simulation of a continuous chemical reactor, (C) 2008 Elsevier Ltd. All rights reserved.