Sufficient conditions for absolute stability and dissipativity of continuous-time recurrent neural networks with two hidden layers are presented. In the autonomous case this is related to a Lur＇e system with multilayer perceptron nonlinearity. Such models are obtained after parameterizing general nonlinear models and controllers by a multilayer perceptron with one hidden layer and representing the control scheme in standard plant Form. The conditions are expressed as matrix inequalities and can be employed for nonlinear Ii, control and imposing closed-loop stability in dynamic back propagation.