The ability to predict meat drip loss by using either near infrared spectra (SPECTRA) or different meat quality (MQ) measurements, such as pH(24), Minolta L(∗), a(∗), b(∗), along with different chemometric approach, was investigated. Back propagation (BP) and counter propagation (CP) artificial neural networks (ANN) were used and compared to PLS (partial least squares) regression. Prediction models were created either by using MQ measurements or by using NIR spectral data as independent predictive variables. The analysis consisted of 312 samples of longissimus dorsi muscle. Data were split into training and test set using 2D Kohonen map. The error of drip loss prediction was similar for ANN (2.2-2.6%) and PLS models (2.2-2.5%) and it was higher for SPECTRA (2.5-2.6%) than for MQ (2.2-2.3%) based models. Nevertheless, the SPECTRA based models gave reasonable prediction errors and due to their simplicity of data acquisition represent an acceptable alternative to classical meat quality based models.