Microwave tomographic imaging techniques have mainly been studied for medical applications in the past two decades. In recent years, however, there has been increased interest in the application of microwave imaging techniques for industrial processes or multiphase flows. In the medical case, water has been used as the background material, with microwave antennas and the object immersed in water, and the contrast of the object is measured against the dielectric properties of water. For industrial application, it is more convenient to use air as the background medium. However, this leads to a large contrast problem if the material being imaged contains a large amount of water. Consequently, the image reconstruction algorithms need to be more adaptive to the level of contrast and uncertainty in the initial guessed values in the iterative reconstruction process. The electromagnetic noise in the open air environment would usually be higher than that in water as a result of the surrounding industrial noise, and the near field region is much larger in air than that in water at the same operating frequency. Therefore, the algorithms need to be less sensitive to the effect of noise. In this paper, two algorithms based on the Newton- Kantorovich and Conjugate Gradient error minimisation methods are investigated with a view to their applications in the imaging of industrial processes using air as the background medium. The results on the effect of noise and the images reconstructed using the algorithms are presented.