The planar grid measurement of the magnetic gradient tensor system （MGTS） is often utilized for magnetic target recognition； however， it is difficult to measure， complicated to analyze， and requires high instrument precision. In this regard， we propose a magnetic target pattern recognition method based on MGTS single heading-line survey. First， the sensitivity of magnetization direction is analyzed for 15 attributes including the components， eigenvalues， and invariants of the magnetic gradient tensor （MGT）. The more sensitive attributes are used to identify target postures， and the insensitive ones are for target shapes. Then， the time-domain signal characteristics of the measured quantities are extracted and the category labels are set. Principal component analysis （PCA） is employed to reduce dimensionality， visualize features， and determine the optimal dimension. Finally， the kernel extreme learning machine optimized by the sparrow search algorithm （SSA-KELM） is used to train and test the survey sample data. The pattern recognition of the magnetic target is hence realized. In the simulation， the recognition of 1） different magnetization direction categories of magnetic dipoles and 2） shape categories of geometric bodies such as the sphere， cuboid， and cylinder is 100% accurate. In the experiment， a total of 180 learning routes were measured for three types of magnets and their corresponding postures. Under the training：testing ratio of 6：4， the results of magnet posture and shape recognition were completely accurate.