The information load (IL) analysis, first introduced for the two-dimensional approach (Carlotti and Magnani, 2009), is applied to the inversion of MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations operated with a 1-dimensional (1-D) retrieval algorithm. The IL distribution of MIPAS spectra is shown to be often asymmetrical with respect to the tangent points of the observations and permits us to define the preferential latitude where the profiles retrieved with a 1-D algorithm should be geo-located. Therefore, defining the geo-location of the retrieved profile by means of the tangent points leads to a "position error". We assess the amplitude of the position error for some of the MIPAS main products and we show that the IL analysis can also be used as a tool for the selection of spectral intervals that, when analyzed, minimize the position error of the retrieved profile. When the temperature (<i>T</i>) profiles are used for the retrieval of volume mixing ratio (VMR) of atmospheric constituents, the <i>T</i>-position error (of the order of 1.5 degrees of latitude) induces a VMR error that is directly connected with the horizontal <i>T</i> gradients. Temperature profiles can be externally-provided or determined in a previous step of the retrieval process. In the first case, the IL analysis shows that a meaningful fraction (often exceeding 50%) of the VMR error deriving from the 1-D approximation is to be attributed to the mismatch between the position assigned to the external <i>T</i> profile and the positions where <i>T</i> is required by the analyzed observations. In the second case the retrieved <i>T</i> values suffer by an error of 1.5–2 K due to neglecting the horizontal variability of <i>T</i>; however the error induced on VMRs is of minor concern because of the generally small mismatch between the IL distribution of the observations analyzed to retrieve <i>T</i> and those analyzed to retrieve the VMR target. An estimate of the contribution of the <i>T</i>-position error to the error budget is provided for MIPAS main products. This study shows that the information load analysis can be successfully exploited in a 1-D context that makes the assumption of horizontal homogeneity of the analyzed portion of atmosphere. The analysis that we propose can be extended to the 1-D inversion of other limb-sounding experiments.