The demand for wireless systems has been growing rapidly over the recent years due tornimproved reliability, high data rates, seamless connectivity and low deployment costs. MIMOrnsystems are the most efficient leading innovation of wireless systems for maximum capacityrnand improved quality and coverage. This theory has been around for a long while but therncomplexity involved and the signal processing required has been a major drawback to its widespreadrnuse. However, recent improvements in Digital Signal Processing (DSP) technology hasrnmade it possible to now construct such transmission systems.rnIn this thesis we study different adaptive blind and nonblind algorithms for MIMO systemsrnsuch as LMS, CMA, SMI, and combined algorithms, LMS-CMA, and SMI-CMA. Moreover,rnwe compare these adaptive array algorithms with other known class of MIMO linear receiverrn(channel estimation) techniques like Zeroforcing (ZF) and minimum mean square errorrn(MMSE) methods. In addition to this, we have discussed Capacity of MIMO systems andrndifferent MIMO transmission techniques such as spatial diversity (SD), Spatialrnmultiplexing(SM).rnThe results of performance evaluation for Adaptive array MIMO receivers revealed that LMSrnhas better BER performance than SMI, SMI-CMA, and ZF and the same performance withrnMMSE with no need of CSI. LMS algorithm has slow convergence but low complexityrncompared to MMSE algorithm that has fast convergence with very high complexity. Moreover,rnthe number of training signals can minimized by 62.5% at the cost of 2-4dB SNR usingrnnonblind algorithm( LMS) combined with blind algorithm( CMA).rnKeywords: Adaptive arrays, MIMO systems, MIMO receivers, blind algorithms, nonblindrnalgorithms, LMS