Inertial sensors, such as accelerometers, gyroscopes and magnetometers, are key components of many modern technologies. They enable the measurement of acceleration, angular velocity and the intensity and direction of magnetic fields and play a crucial role in various industries such as marine-based shipping, automotive, aerospace, robotics, medical technology and consumer electronics. Their ability to make precise measurements has enabled the development of many advanced systems, from autonomous ships, vehicles and navigation systems to smartphones and medical devices. Despite their widespread use, the accuracy and reliability of inertial sensors are often limited by various types of errors that have a negative impact on measurement quality. These errors can be categorised into two main groups: systematic (deterministic) and random (stochastic) errors. While systematic errors can be identified and reduced through calibration, random errors require more complex approaches to modelling and compensation. Systematic errors are usually due to inadequacies in the manufacturing process and affect sensor performance. Their effects can be mitigated by comparing the measured values with reference standards. Random errors, on the other hand, require stochastic modelling as they originate from unpredictable sources such as noise. Their most notable feature is the lack of a direct relationship between input and output. To ensure the reliability and accuracy of measurements, proper calibration of these sensors is crucial. Calibration procedures reduce errors, adjust the output data and improve the performance of the sensors. Numerous calibration methods have been developed, ranging from traditional laboratory techniques using high-precision reference devices to modern approaches based on mathematical modelling and data processing algorithms. Common methods include static and dynamic calibration, which include processes such as sensor rotation, the use of precise reference systems and data analysis for error correction. In addition, advanced methods based on machine learning algorithms and optimisation techniques are increasingly being used to improve sensor accuracy. This paper provides an overview of calibration methods for inertial sensors. The aim is to provide a concise yet comprehensive insight into existing calibration approaches, with a focus on improving the accuracy and reliability of sensor systems for marine applications.