NNV (Neural Network Verification) is a framework for the verification of deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS) inspired by a collection of reachability algorithms that make use of a variety of set representations such as the star set. NNV supports exact and over-approximate reachability algorithms used to verify the safety and robustness of feed-forward neural networks (FFNNs). These two analysis schemes are also used for learning enabled CPS, i.e., closed-loop systems, and particularly in neural network control systems with linear models and FFNN controllers with piecewise-linear activation functions. Additionally, NNV supports over-approximate analysis for nonlinear plant models by combining the star set analysis used for FFNNs with the zonotope-based analysis for nonlinear plant dynamics provided by CORA. This demo paper demonstrates NNV's capabilities by considering a case study of the verification of a learning-enabled adaptive cruise control system.