Homogenization of climate data is of major importance because non-climatic factors make available data unrepresentative of the actual climate variation, and thus the conclusions of climatic and hydrological studies are potentially biased. A great deal of effort has been made to develop procedures to identify and remove non-climatic in-homogeneities. This paper first reviews several widely used statistical techniques then applies statistical simulation approach to precipitation data from different monitoring stations located in Kenya (1950-2006). Analyses were carried out on several rainfall series in the 12 climatic zones of Kenya. The results of both the Standard Normal Homogeneity Tests (SNHT) and the Buishand Range Test (BR) tests show that, at the 5% significance level, the monthly series have statistically significant trend. Findings from the Standard Normal Homogeneity Test (SNHT) showed that all the monthly rainfall records from the selected synoptic stations were useful and hence could be used for any further analysis. From the Buishand Range (BR) Test done, seven out of the twelve stations were useful while the rest of the stations were doubtful. From the results of the Tests performed it is clear that the Buishand Range (BR) Test was able to detect breaks at the beginning middle and the end of the series. This method was thus recommended for homogeneity testing. Promising results from the case study open new research perspectives on the homogenization of the Kenyan climate data time series.