We propose Characteristic-Neural Ordinary Differential Equations (C-NODEs), a framework for extending Neural Ordinary Differential Equations (NODEs) beyond ODEs. While NODE models the evolution of latent variables as the solution to an ODE, C-NODE models the evolution of the latent variables as the solution of a family of first-order partial differential equations (PDEs) along curves on which the PDEs reduce to ODEs, referred to as characteristic curves. This reduction along characteristic curves allows for analyzing PDEs through standard techniques used for ODEs, in particular the adjoint sensitivity method. We also derive C-NODE-based continuous normalizing flows, which describe the density evolution of latent variables along multiple dimensions. Empirical results demonstrate the improvements provided by the proposed method for irregularly sampled time series prediction on MuJoCo, PhysioNet, and Human Activity datasets and classification and density estimation on CIFAR-10, SVHN, and MNIST datasets given a similar computational budget as the existing NODE methods.The results also provide empirical evidence that the learned curves improve the system efficiency using a lower number of parameters and function evaluations compared with those of the baselines.