The aim of this study was to present a method for sediment texture characterization combining three spectroscopy techniques with multivariate analysis. Specifically, data were used from flame atomic absorption spectroscopy (FAAS), energy dispersive X-ray fluorescence (EDXRF) and photoacoustic spectroscopy (PAS) measurements in superficial lake sediment samples, which were combined with principal component analysis for exploratory data analysis and with partial least square (PLS) regression for indirect estimative of clay and sand textures . It was possible to infer quantitative values of grain size using the data from the three techniques allied with PLS regression, which allowed to obtain implicit information in each data set. The same classifications in accordance with samples texture were verified for all the applied spectroscopic methods. However, the classification by EDXRF or PAS was simpler, faster, cheaper and non destructive when compared with FAAS. In the case of granulometry quantitative prediction by PLS regression, the results for PAS were less accurate, but were satisfactory for EDXRF and FAAS. In general the results are promising, indicating the method viability, although a larger sampling is necessary to implement the methodology.