Ordinary differential equations are introduced as continuous-time dynamical systems at the begining of the thesis. Distinction of fixed points and their stability are necessary steps for determining behavior of systems. There are two types of ordinary differential equations: linear and nonlinear. Most nonlinear differential equations are not solved analytically. Linear systems can be broken down into parts, then each part can be solved separately and finally recombined to obtain the answer but nonlinear systems cannot, it is the main difference between linear and nonlinear systems. We have tried to use a geometrical method for studying of qualitative behavior of linear systems in phase space. Then the method have been expanded to nonlinear systems for determine their behavior near the fixed points to find general scheme in phase space. In addition numerical methods are used to show oscillators' trajectories in phase space. After that chaotic dynamical systems are described. These oscillators have strange attractors, they are very sensitive in initial conditions and their irregularities are because of their nonlinear dynamics without any random term in their equations. A system can include the features when it has at least one negative Lyapunov exponent and one positive Lyapunov exponent. Therefore continuous- time chaotic oscillators are three dimensional at least. Then we have studied synchronization of chaotic oscillators as a collective behavior and have shown that the oscillators could be coupled in networks. We can define time average between similar events as average period of a chaotic oscillator and phase is defined corresponding their trajectories (zero Lyapunov exponent) in phase space. When oscillators affect on each other in networks, a simplest way to quantify coherence in a networks is to use order parameters. At last stability of complete synchronization in networks of chaotic systems is studied. In the synchronous suace that is termed synchronization manifold, oscillators' dynamical variables are equal. Synchronization can be observed in physical universe if the manifold be stable with respect to perturbations in the transverse suace. The Master Stability Function (MSF) that is the largest transverse Lyapunov exponent of the synchronization manifold, measures the exponential rate of an infinitesimal perturbation in the transverse suace. A necessary condition for synchronization to occur is that the MSF be negative and corresponding normalized coupling parameters fall in the negative region of the MSF. Previously stability of complete synchronization has been studied for networks that summation of elements of each coupling matrix's row is zero. We have proved complete synchronization can be stable in networks that summation of elements of each coupling matrix's row is constant, then we have used MSF method for these types. Keywords: Dynamic, Chaos, Synchronization, Synchronization manifold, Normalized coupling parameters, Master Stability Function.