This thesis focusses on harnessing unique properties of chaotic signals to perform blind channel equalization for direct chaotic spread spectrum and differential chaos shift keying spread spectrum communication systems. The contribution of the thesis is two-fold. Firstly, we compare their performance for an indoor frequency selective fading channel using experimental data from software defined radio (SDR). Secondly, the existing chaos based system identification methods such as minimum phase space volume, minimum non linear prediction error, extended Kalman filter were extended to equalize a multipath fading channel. A comparison is made to test the performance of chaos based blind equalization methods with conventional constant modulus algorithm (CMA) using computer simulations and by processing experimental data from SDR in an off-line mode. The results show that chaos based blind equalization methods can give better performance than conventional CMA.