The paper presents a method for estimating non-negative Lyapunov exponents from experimental time series data. Lyapunov exponents quantify the rate of divergence or convergence of nearby orbits in phase space, and a system with one or more positive exponents is chaotic. The method is based on monitoring the long-term growth rate of small volume elements in an attractor. It is tested on model systems with known Lyapunov spectra and applied to data from the Belousov-Zhabotinskii reaction and Couette-Taylor flow. The method involves reconstructing phase space from time series data using delay coordinates and estimating exponents by tracking the growth of volume elements. The algorithm is implemented in Fortran and includes programs for estimating the largest exponent and the sum of the two largest exponents. The method is robust and can be applied to experimental data with noise, though the accuracy depends on the quality and quantity of data. The paper discusses the selection of embedding dimension and delay time, the evolution times between replacements, and the accumulation of orientation errors. The results show that the method provides accurate estimates of Lyapunov exponents for chaotic systems.The paper presents a method for estimating non-negative Lyapunov exponents from experimental time series data. Lyapunov exponents quantify the rate of divergence or convergence of nearby orbits in phase space, and a system with one or more positive exponents is chaotic. The method is based on monitoring the long-term growth rate of small volume elements in an attractor. It is tested on model systems with known Lyapunov spectra and applied to data from the Belousov-Zhabotinskii reaction and Couette-Taylor flow. The method involves reconstructing phase space from time series data using delay coordinates and estimating exponents by tracking the growth of volume elements. The algorithm is implemented in Fortran and includes programs for estimating the largest exponent and the sum of the two largest exponents. The method is robust and can be applied to experimental data with noise, though the accuracy depends on the quality and quantity of data. The paper discusses the selection of embedding dimension and delay time, the evolution times between replacements, and the accumulation of orientation errors. The results show that the method provides accurate estimates of Lyapunov exponents for chaotic systems.