Open and distance learning programs have gained worldwide popularity in recent years.  Accessibility and flexibility, which are the direct consequences of the latest technological developments, have played a great role in this process. The overall student population of distance education programs around the world includes learners from different countries and cultures. However, a significant number of learners do not complete or quit open and distance learning programs too early due to a various reasons.  The purpose of this paper is to analyze the reasons and suggest possible solutions for the dropout problem in distance education programs. It appears that the most common reasons for dropout in open and distance learning may be personal, environmental, social, psychological, even pedagogical. The models that explain the general dropout phenomenon in education may be applied as operational frameworks to understand the specific roots and characteristics of dropout in distance education. The potential of data mining also deserves particular attention within the context of lowering dropout rates. Considering that there is a relatively limited number of studies examining the dropout problem in distance education, the present paper is expected to provide a particular contribution to the literature in this area.