All elements of climate that affect climatic events must be taken into account such that the climate regions are determined with exactitude. To this end, data on maximum temperature (Tx), minimum temperature (Tn), mean temperature (Tm), and precipitation (Pt) as well as local pressure (Ps), mean wind (WN), relative humidity (RH), and specific humidity (SH) have been investigated statistically and graphically. The specific humidity data calculated using Tm, Ps, and RH data and statistical comparisons have shown that there are no drawbacks to using SH in climatologic studies. According to principal component analysis, it was concluded that RH and SH should be used together with Tx, Tm, Tn, and Pt for the determination of the climate regions. Two cluster analysis methods, Ward's method and Kohonen neural network technique, were used to show the effect of RH and SH. A comparison of the cluster's stability between the limited and high number of stations shows that Ward's method and Kohonen neural network are very stable in both cases. It was also determined that RH does not change the outline of climate regions but that it affects the zones of climate transition. It was observed that clusters determined by using Tm, Pt, and RH provide relatively more distinctive clusters in the data space than clusters determined by using Tm, Pt, and SH.