The thesis investigates chemometric methods that could be applied to near-infrared (NIR) spectroscopic data to monitor and control efficiently industrial batch processes. Monitoring and controlling processes is a key activity since it guarantees that the manufactured products will meet the required specifications. To perform this control, several kinds of analyses exist. The in-line analysis that makes the analysis directly in the process line without taking any sample is the one that gives the most immediate information about the process quality. This thesis focuses exclusively on methods that can be applied in-line. NIR spectroscopy is described in chapter 1 and some candidate methods are presented in chapter 2. The Orthogonal Projection Approach (OPA) is especially studied because of its simplicity and because of the interpretability of the results. Batch data present also an n-way structure (typically samples x variables x batches) and methods able to deal with them are also described in chapter 2. To control processes in-line, models can be used. In the thesis, using curve resolution method such as OPA does not consist, in the theoretical sense, in building models but in finding simple meaningful variables that represent and describe as closely as possible the original data. Practically, although this is not strictly correct, this representation can also be considered as a model. However, while modelling, problems can occur, especially about complexity. The complexity problem is a general problem in chemometrics and means to deal with it are described in chapter 3. Another important topic of this thesis is the selection of appropriate data. Part B of chapter 3 is about the selection of NIR wavelengths able to reproduce similar OPA results as with complete spectra. This is important from an industrial point of view to decrease the NIR spectra acquisition time, and consequently also the time needed to obtain predictions via models. Genetic algorithms are here used for this purpose. Chapter 4 is about the use of OPA to monitor batch processes in an industrial context. It gives an overview of the industrial data, of the way the OPA model is computed and of the obtained results. Some Multivariate Statistical Process Control (MSPC) statistics based on Principal Components Analysis (PCA) are also investigated in the OPA context in chapter 4. In chapter 5, a method called STATIS is described to monitor the evolution in time of a batch. Another approach using the Hausdorff distance is also proposed in the same chapter. Chapter 6 deals with some limitations of OPA for batch process data, especially when rank deficiency occurs. The problem is examined and some solutions are given.