The problem of direct interpretation of apparent resistivity curves from horizontally layered models is accomplished by using the generalized linear inverse theory. The method permits the resolution of the model parameters to be determined. The method also indicates which data points contain relatively important information necessary to resolve the model parameters. Two models were chosen to test the inversion scheme. One model has increasing resistivity with depth, and the other model possesses an intermediate resistive layer. Both models were resolved with a very high degree of accuracy from noise‐free data. When noise was added to the data, the values of the parameters oscillated about a mean value. The noise had little effect on the well‐resolved parameters but the poorly resolved parameters were in error by as much as 15 percent. The importance of each data point relative to the model was analyzed. The effect of certain data points on specific parameters was also determined. The generalized inverse method requires that the eigenvalues and eigenvectors of the system matrix be found. A comparison of the eigenvalues indicates those parameters that are well‐resolved and those that are poorly resolved from a given set of data.