LiDAR (Light detection and ranging) point-cloud data contain abundant three-dimensional (3D) spatial information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical LiDAR dataset of artificial structures. Plane fitting is the key process for extracting plane features from LiDAR data. This paper investigates the two mostly applied plane fitting methods: Least Squares Fitting (LSF) and Principal Component Analysis (PCA). LSF is an iterative algorithm of finding the best-fit plane with the least-squares constraint of the distances from the scanned points to the plane. The PCA method calculates the eigenvector of point cloud as the normal for finding out the parameters of the optimal plane. The goal of this paper is to compare these two methods in terms of algorithm implementation, computation time, and robust estimation. Experimental results of computations with simulated data are shown for analysis.