In practice, two problems, limited batches and irregular batch trajectories, may simultaneously exist in batch processes, making process modelling and monitoring a great challenge. However, previous work did not solve the two problems simultaneously. Motivated by such cognition, for those processes with limited modelling batches and irregular batch durations, this work proposes a LV (Latent Variable)-based concurrent multi-trends analysis method, which combines the advantages of multi-set variable correlation analysis and trend analysis. First, cross set correlation analysis is implemented to reduce the variable dimensionality and extract the latent variables. Then, temporal evolution trends of latent variables are described by multiple polynomials, formulating multi-trends models. For online monitoring, the sequential nature also provides an easy but effective way to check the operation status of a new sample. Additionally, a trends based updating strategy is proposed to accommodate normal batch-wise variations to develop a more reliable monitoring system. The application to a typical batch process with uneven-length and limited batches illustrates the online monitoring performance of the proposed method.