Abstract: A novel neural network model, named delayed standard neural network model (DSNNM), is proposed, which is the interconnection of a linear dynamic system and a bounded static delayed nonlinear operator. By combining a number of different Lyapunov functionals with SProcedure, some sufficient conditions for global asymptotic stability and global exponential stability of the DSNNM are derived and formulated as linear matrix inequalities (LMIs). Most delayed (or non-delayed) dynamic artificial neural networks (DANNs) or neuro-control systems can be transformed into DSNNMs so that stability analysis or stabilization synthesis can be done in a unified way. In this paper, DSNNMs are applied to nalyzing the stability of the delayed bidirectional associative memory (BAM) neural net- works and synthesizing the neuro-controllers for the PH neutralization process. The stability criteria obtained turn out to be a generalization of some previous criteria. The analysis ap- proach is further extended to the nonlinear control system.