We introduce a set of statistical measures that can be used to quantify non-equilibrium surface growth. They are used to deduce new information about spatiotemporal dynamics of model systems for spinodal decomposition and surface deposition. Patterns growth in the Cahn-Hilliard Equation (used to model spinodal decomposition) are shown to exhibit three distinct stages. Two models of surface growth, namely the continuous Kardar-Parisi-Zhang (KPZ) model and the discrete Restricted-Solid-On-Solid (RSOS) model are shown to have different saturation exponents.PACS numbers: 47.54.+r,47.20.Hw,05.70.Np,64.75.+g,89.75.Kd,05.50.+q, In recent years, there has been a considerable amount of effort to analyze non-equilibrium interfacial growth and pattern formation in experimental and model systems [1,2,3,4,5,6,7,8,9,10]. The phenomena studied include spinodal decomposition [1,5,11,12], chemical pattern formation [4], surface growth [2,3,7,8] and epitaxial growth [9,10]. Microscopic modeling of these phenomena is highly complex, and most microscopic details of such structures depend on initial conditions and stochastic effects. Hence model systems are often used to extract statistical properties of these structures and to determine the physical processes that are most relevant for their growth. Some of the model systems that have been introduced to study the spatiotemporal dynamics of the aforementioned systems are the Cahn-Hilliard Equation (CHE) [1], the Kardar-ParisiZhang (KPZ) model [2], the Restricted-Solid-On-Solid (RSOS) model [3], and the Swift-Hohenberg Equation (SHE) [4,13].In order to confirm if a given model can accurately represent a pattern forming process, it is necessary to compare as many statistical measures as possible. Such a comparison between model systems and physical systems is also needed to validate claims of universality. Unfortunately, there are only a handful of measures that are available to be used for such comparisons. The aim of this work is to introduce a new family of such measures.Commonly used statistical measures to analyze surface growth and patterns include surface roughness (i.e., the standard deviation of the heights) W L (t), where L is the lattice size and t the time, the correlation length [8], and the domain size [11,12]. For the KPZ and RSOS interfaces,The growth exponent β, the dynamic exponent z, and the roughness exponent α, depend only on the dimensionality of the growth process and are independent of L apart from finite-size scaling corrections * Electronic address: Girish.Nathan@mail.uh.edu † Electronic address: Gemunu@mail.uh.edu [6]. It has been found numerically that these exponents are the same in all dimensions for the KPZ and the RSOS models. Based on the results, it has been suggested that KPZ and RSOS belong to the same universality class. This is a non-trivial statement, since although both models consider the competition between random deposition and diffusion, RSOS is a discrete counterpart of the KPZ with distinct microscopic dynamics. The availability of additio...