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In this paper, the evaluation of wireless sensor network (WSN) cluster head selection for different propagation environments based on Lee path loss model and K-means algorithm is presented. A set of 50 WSN nodes distributed randomly over an area of 3000 m by 3000 m with the base station located in the middle of the area, at the coordinate of x= 1500 m and y= 1500 m. The X and y coordinates of each of the 50 WSN nodes were generated using Matlab software random number generators function. The coordinate geometry formula for distance, d between two points with coordinates,(𝑥1, 𝑦1) and (𝑥2, 𝑦2) is used to compute the distance from each of the nodes to the base station and then the distances were used to compute the path loss and hence the received signal strength (RSS) for each of the nodes. The computation was conducted for the three different propagation environments, as specified in the Lee model, namely, the urban, the suburban and the rural or open area. Based on RSS (-60 dBm≤ RSS≤-90 dBm), candidate cluster heads were selected for the urban, the suburban and the rural or open area. Afterwards, the K-means clustering algorithm was used to select the cluster heads from the candidate cluster heads. The results show that for the urban area, only 4 nodes were selected as cluster heads; the node with a received signal strength of-60.8 dBm had the highest number of 17 slave nodes clustered around it whereas, the node with a received signal strength of-91.7 dBm had the lowest number of 10 slave nodes clustered around it. Also, for the open or rural area, 12 nodes were selected as cluster heads; the node with a …