Conventional technique of computing, which is centrally based on mathematical approaches to problem solving, is considered as hard computing technique. This requires precisely stated analytical model. Many analytical models are available for ideal cases. However, real world problems exist in a nonideal environment. Soft computing techniques, which have drawn their inherent characteristics from biological systems, present effective solutions to these problems. Soft computing refers to computational techniques that include Fuzzy Logic, Artificial Neural Networks and Genetic Algorithms. All of these techniques find their deep roots in artificial intelligence (AI). The paper highlights important features of these computational techniques. Each of these techniques, in their domain has provided very effective solutions to wide range of problems. Attempts have been made to integrate features of these techniques. The paper also focuses on systems of such hybrid soft computing techniques in brief and it is argued that these systems must be autonomous, robust and adaptive in order to be intelligent and stable. Finally, this paper clearly brings out superiority of soft computing techniques over the conventional. (5 pages)